{"meta":{"query_hash":"88f1e3330a18","filters":{"venue":"INFORMS Journal on Applied Analytics"},"cohort_total":83,"direct_labels_cover":0,"predictions_cover":83,"exported":83,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/88f1e3330a18","api":"https://metacan.xera.ac/api/v1/cohort?venue=INFORMS+Journal+on+Applied+Analytics"},"results":[{"id":"W1972551986","doi":"10.1287/inte.2013.0716","title":"Scotsburn Dairy Group Uses a Hierarchical Production Scheduling and Inventory Management System to Control Its Ice Cream Production","year":2014,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture; Dalhousie University","funders":"","keywords":"Operations research; Production planning; Aggregate planning; Scheduling (production processes); Schedule; Production (economics); Synchronizing; Term (time); Integer programming; Operations management; Computer science; Engineering; Economics; Microeconomics; Algorithm","score_opus":0.010499036831239969,"score_gpt":0.21711851220491413,"score_spread":0.20661947537367414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972551986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34221566,0.0001885706,0.6434476,0.0007454047,0.0031195055,0.0011182424,0.0000037087534,0.0008334403,0.008327898],"genre_scores_gemma":[0.96940243,0.00013069235,0.028914893,0.0002518356,0.0011153684,0.000027020891,0.0000052765095,0.000044981472,0.00010751475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984473,0.000021003469,0.00053931127,0.00023871286,0.00043824763,0.0003154228],"domain_scores_gemma":[0.99922204,0.000027760721,0.00013229986,0.0002236895,0.00010222207,0.00029197428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084326573,0.00023855438,0.00028005117,0.0004576479,0.00023050571,0.00021822567,0.00014269921,0.00009822904,0.0000045741003],"category_scores_gemma":[0.00010382657,0.0002055914,0.000058515212,0.00038318365,0.000030904124,0.00023332439,0.00002832997,0.00048131202,0.00006361389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007862137,0.000044427317,0.000064191256,0.0002471482,0.00015006839,0.0000037304364,0.00020998521,0.976084,0.00021079463,0.007081101,0.00016141715,0.015664527],"study_design_scores_gemma":[0.001233256,0.00018408652,0.00042229157,0.00047656999,0.00016251992,0.00015730776,0.00082478806,0.9915546,0.00083791395,0.00021429049,0.0034676215,0.00046476393],"about_ca_topic_score_codex":4.983814e-7,"about_ca_topic_score_gemma":0.0000010090868,"teacher_disagreement_score":0.6271868,"about_ca_system_score_codex":0.00020821851,"about_ca_system_score_gemma":0.000014154966,"threshold_uncertainty_score":0.8383774},"labels":[],"label_agreement":null},{"id":"W1976415148","doi":"10.1287/inte.2013.0728","title":"Introduction: 2013 Franz Edelman Award for Achievement in Operations Research and the Management Sciences","year":2014,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Corporate Governance and Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Universiteit van Tilburg; Deltares","keywords":"Management; Operations research; Engineering; Engineering management; Computer science; Economics","score_opus":0.03953833893511348,"score_gpt":0.2854690114059198,"score_spread":0.2459306724708063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976415148","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2653944,0.00022026461,0.07453829,0.3470679,0.0029119558,0.0066112066,0.000005923682,0.00015178502,0.3030983],"genre_scores_gemma":[0.9834159,0.0006692048,0.0015131822,0.006653318,0.0043667215,0.00018313275,0.000011791154,0.000023017245,0.003163757],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.998292,0.0000083445875,0.00045425165,0.00023386179,0.0006377368,0.00037378573],"domain_scores_gemma":[0.9993694,0.000057472982,0.00015952736,0.00022239385,0.00017018974,0.000021015314],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0041751475,0.00015169813,0.00020152132,0.00054980547,0.0009424473,0.0011266872,0.00038465858,0.000035292287,0.000039575538],"category_scores_gemma":[0.000037394275,0.00009253866,0.00005484877,0.00076011167,0.0002754184,0.0005836347,0.0002263645,0.0003022333,0.000092862036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015186793,0.00007485448,0.00015124016,0.00005717184,0.00004328453,0.0000012172602,0.00004702646,0.022906166,0.0000030012686,0.9396114,0.024442872,0.012509952],"study_design_scores_gemma":[0.002949678,0.000079963895,0.0013876548,0.000042003707,0.000047370657,0.000002059808,0.0013259504,0.048719656,0.0000074216528,0.07748961,0.86776227,0.00018633762],"about_ca_topic_score_codex":0.00007755354,"about_ca_topic_score_gemma":0.00026454366,"teacher_disagreement_score":0.86212176,"about_ca_system_score_codex":0.00007338894,"about_ca_system_score_gemma":0.00001774133,"threshold_uncertainty_score":0.99991024},"labels":[],"label_agreement":null},{"id":"W1979906780","doi":"10.1287/inte.1110.0544","title":"Designing New Electoral Districts for the City of Edmonton","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal; University of Alberta","funders":"","keywords":"Redistricting; Contiguity; Plan (archaeology); Heuristic; Operations research; Population; Computer science; Process (computing); Transport engineering; Tabu search; Engineering; Geography; Legislature; Sociology; Artificial intelligence","score_opus":0.062465748116388636,"score_gpt":0.27094747060065544,"score_spread":0.2084817224842668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979906780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007467648,0.000042003943,0.9855805,0.000017389986,0.00015079923,0.0001517102,0.000002670184,0.000058513317,0.006528786],"genre_scores_gemma":[0.8298822,0.00005180974,0.16969709,0.000068371126,0.00017282242,0.0000033110896,0.0000017452355,0.000027167882,0.00009547767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905443,0.000005983178,0.00043742705,0.00005984816,0.00020499698,0.0002372897],"domain_scores_gemma":[0.99925387,0.00019427047,0.00018766201,0.00016422108,0.00008262802,0.00011734389],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005405441,0.00013497222,0.00019316551,0.000109503504,0.000104657025,0.000046899426,0.0002483232,0.000074714626,0.000043158758],"category_scores_gemma":[0.00010274739,0.0000903037,0.00010513343,0.00028184627,0.000024909787,0.000093431314,0.000014686465,0.00029443557,0.0000036584754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019559698,0.00004582891,0.002442139,0.000059798727,0.00040031923,0.0000025893746,0.0013477848,0.8861067,0.0023817504,0.009102204,0.0024932225,0.09542202],"study_design_scores_gemma":[0.002830347,0.00042355372,0.0104589565,0.00010582011,0.00039055105,0.000050212322,0.00044985232,0.8451977,0.12391708,0.0075374306,0.00786624,0.00077230384],"about_ca_topic_score_codex":0.000005527853,"about_ca_topic_score_gemma":0.0000023650218,"teacher_disagreement_score":0.8224146,"about_ca_system_score_codex":0.00007827838,"about_ca_system_score_gemma":0.00006107858,"threshold_uncertainty_score":0.3682478},"labels":[],"label_agreement":null},{"id":"W1982221171","doi":"10.1287/inte.1110.0561","title":"A Decision Support System for Scheduling the Canadian Football League","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan; Federated Co-operatives (Canada)","funders":"","keywords":"League; Football; Schedule; Operations research; Scheduling (production processes); Computer science; Decision support system; Business; Engineering; Operations management; Advertising; Political science; Artificial intelligence","score_opus":0.11606419874026568,"score_gpt":0.36549903321524857,"score_spread":0.24943483447498288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982221171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14713539,0.00043968242,0.65964466,0.0061726067,0.01074809,0.0016544933,0.00014601355,0.00023805228,0.173821],"genre_scores_gemma":[0.98177457,0.0000083693585,0.015124447,0.0011324544,0.0010130819,0.000015532918,0.0000053992785,0.000023349581,0.0009027949],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99578696,0.00003123491,0.0012381129,0.00022453202,0.0017088414,0.0010103029],"domain_scores_gemma":[0.9952375,0.0018439571,0.00060977,0.0006679184,0.00065149745,0.0009893356],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011528761,0.00024009864,0.00039823868,0.0008415975,0.00202481,0.0012675398,0.001144488,0.00019885931,0.00009427508],"category_scores_gemma":[0.0026675004,0.00012863512,0.00037994407,0.0010450255,0.000102883605,0.00038797266,0.00006071559,0.0006980872,0.0011319881],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040909302,0.00018707474,0.0073609906,0.00002368416,0.0004788453,0.000019226269,0.002361416,0.16275816,0.000069431866,0.61319125,0.044261064,0.16887976],"study_design_scores_gemma":[0.002613117,0.0002983762,0.0035235318,0.00014725747,0.00035282128,0.0007640495,0.008663065,0.09188632,0.00050120597,0.0335631,0.8567866,0.00090056064],"about_ca_topic_score_codex":0.00040368355,"about_ca_topic_score_gemma":0.003741525,"teacher_disagreement_score":0.8346392,"about_ca_system_score_codex":0.00054380717,"about_ca_system_score_gemma":0.0009213132,"threshold_uncertainty_score":0.9997692},"labels":[],"label_agreement":null},{"id":"W1988340906","doi":"10.1287/inte.1070.0316","title":"<b>Call for Papers</b>—<i>Interfaces</i> Special Issue: Applications of Management Science and Operations Research Models and Methods to Problems in Health Care","year":2007,"lang":"en","type":"paratext","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Health care; Government (linguistics); Computer science; Operations research; Management science; Engineering; Political science","score_opus":0.11969148278153804,"score_gpt":0.526337057688987,"score_spread":0.4066455749074489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988340906","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00034859678,0.00092107547,0.64941883,0.005379527,0.00096998893,0.014719157,0.00030685414,0.000023101997,0.3279129],"genre_scores_gemma":[0.019485798,0.02172157,0.8890825,0.00914151,0.004882168,0.003148781,0.00042369525,0.00020323033,0.051910773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955665,0.00018701337,0.0019413279,0.0005020155,0.00092486,0.0008782986],"domain_scores_gemma":[0.9962159,0.00039312252,0.00038096737,0.00039879137,0.0020425727,0.0005686578],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.010815871,0.00028329526,0.0006705504,0.0022015984,0.0023462593,0.00018673358,0.00040520084,0.00036935162,0.0001114749],"category_scores_gemma":[0.00015552528,0.00023662782,0.000042544732,0.0017856547,0.00025648298,0.0001967272,0.00023547791,0.0018030149,0.00005699943],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021757872,0.000166374,0.000014267636,0.005258165,0.000085488704,9.265697e-7,0.029152349,0.59334874,0.00007099281,0.05901957,0.052423116,0.2602424],"study_design_scores_gemma":[0.0014265706,0.0005651611,0.000031166124,0.0015584185,0.000030162337,0.00000416098,0.020458365,0.025422154,0.000047357844,0.00062016683,0.94943374,0.00040259215],"about_ca_topic_score_codex":0.00013545556,"about_ca_topic_score_gemma":0.0006223163,"teacher_disagreement_score":0.8970106,"about_ca_system_score_codex":0.001323511,"about_ca_system_score_gemma":0.0024880052,"threshold_uncertainty_score":0.99895257},"labels":[],"label_agreement":null},{"id":"W1990846384","doi":"10.1287/inte.32.2.63.57","title":"Mount Sinai Hospital Uses Integer Programming to Allocate Operating Room Time","year":2002,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":212,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mount; Integer programming; Schedule; Operations research; Heuristic; Integer (computer science); Operations management; Branch and price; Computer science; Linear programming; Mathematical optimization; Engineering; Operating system; Mathematics; Artificial intelligence; Algorithm","score_opus":0.05208496301005444,"score_gpt":0.3588725518668935,"score_spread":0.30678758885683904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990846384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76721483,0.000119148965,0.13211332,0.0298349,0.002118891,0.0044744946,0.000036492962,0.0005443545,0.06354354],"genre_scores_gemma":[0.9551549,0.00009127403,0.033099446,0.006915591,0.00079948985,0.00007759222,0.00002603559,0.000059066315,0.0037765845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973534,0.000063158914,0.0011736432,0.00022464439,0.00048258406,0.0007025813],"domain_scores_gemma":[0.9981737,0.00014644828,0.0003505247,0.0002809137,0.00055245927,0.0004959651],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009760381,0.000257179,0.00036000137,0.0003096057,0.0016781505,0.00026946736,0.00024726288,0.0002300527,0.00088616344],"category_scores_gemma":[0.00036345044,0.00019172733,0.00008834409,0.00054123625,0.000025272488,0.00031958695,0.000076175886,0.0013259082,0.0026746802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001680515,0.0011279096,0.0067937416,0.00032323212,0.00043542378,0.000112459544,0.0471391,0.6481419,0.00073240773,0.045734137,0.04454454,0.20474708],"study_design_scores_gemma":[0.0027659773,0.0017454713,0.0010899851,0.0011968459,0.00011444869,0.000048617083,0.011319352,0.669185,0.00015055925,0.00030447388,0.3105904,0.0014888647],"about_ca_topic_score_codex":0.00002603851,"about_ca_topic_score_gemma":0.00001527866,"teacher_disagreement_score":0.26604587,"about_ca_system_score_codex":0.0006304644,"about_ca_system_score_gemma":0.00023216287,"threshold_uncertainty_score":0.9996215},"labels":[],"label_agreement":null},{"id":"W1991773164","doi":"10.1287/inte.1110.0612","title":"Quantifying the Contribution of NHL Player Types to Team Performance","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Salary; League; Cluster analysis; Value (mathematics); Computer science; Artificial intelligence; Machine learning; Economics","score_opus":0.04719951990944064,"score_gpt":0.2540716229958695,"score_spread":0.20687210308642884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991773164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9441438,0.0004045662,0.0039288225,0.0004304937,0.00064596283,0.00021825511,0.00003171743,0.000016163638,0.050180238],"genre_scores_gemma":[0.99778134,0.00048426553,0.00014152333,0.00086347613,0.0003718143,0.000004230346,0.0000055778823,0.000015633404,0.00033214453],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845225,0.000001939929,0.00088374515,0.00011040437,0.00012967875,0.0004219661],"domain_scores_gemma":[0.9987002,0.00006817619,0.000689552,0.0002771972,0.00009023465,0.0001746241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387319,0.00016187926,0.0003664054,0.0002849734,0.00023799125,0.00009128054,0.00028957298,0.0000845841,0.00023182703],"category_scores_gemma":[0.00009195422,0.000111848785,0.00013030437,0.00038918213,0.00004655518,0.0002772817,0.00004516194,0.00035927928,0.00061648514],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011816985,0.0001875779,0.50019085,0.000029352028,0.00018121203,7.7831265e-7,0.001000308,0.02399131,0.00006107754,0.46659744,0.0034237278,0.004218216],"study_design_scores_gemma":[0.0014227721,0.00051207526,0.25413266,0.00011806849,0.000078829966,0.0000623416,0.00046983088,0.05776618,0.002966546,0.0024299258,0.6792177,0.00082308555],"about_ca_topic_score_codex":0.000007721765,"about_ca_topic_score_gemma":0.0000038992625,"teacher_disagreement_score":0.67579395,"about_ca_system_score_codex":0.00010576966,"about_ca_system_score_gemma":0.000028278293,"threshold_uncertainty_score":0.7923875},"labels":[],"label_agreement":null},{"id":"W1995161114","doi":"10.1287/inte.2014.0776","title":"Introduction: 2014 Franz Edelman Award for Achievement in Operations Research and the Management Sciences","year":2015,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Centers for Disease Control and Prevention","keywords":"Competition (biology); Management; Analytics; Cover (algebra); Engineering; Operations research; Computer science; Data science; Economics","score_opus":0.14554763526424086,"score_gpt":0.3621413515305761,"score_spread":0.21659371626633522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995161114","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23161508,0.0008136676,0.12360106,0.33467522,0.006151044,0.007089027,0.00002466807,0.00020063581,0.2958296],"genre_scores_gemma":[0.9847768,0.00051202916,0.0026134353,0.004129886,0.005893167,0.00012596199,0.000034264653,0.000023884855,0.0018905593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983418,0.000006724033,0.00043026006,0.00020840036,0.00067842274,0.00033439684],"domain_scores_gemma":[0.999258,0.00005149797,0.000099726574,0.00021113407,0.00035026085,0.000029373225],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004511813,0.00013454219,0.00017689817,0.0006008356,0.0007137664,0.0012672673,0.00047180703,0.000045293797,0.000047333695],"category_scores_gemma":[0.00008086774,0.000077308316,0.000036529353,0.00085432996,0.00041983413,0.0008108046,0.0002177637,0.0003393102,0.00013917444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030186574,0.00010228529,0.00034626725,0.00005138116,0.0000391626,0.0000023633531,0.00009066799,0.018280672,0.0000030679378,0.92516875,0.046035256,0.009578289],"study_design_scores_gemma":[0.0025772334,0.0000763716,0.00057562324,0.00005343352,0.000049444254,0.000011280983,0.003434041,0.04186579,0.000030162118,0.0794122,0.871677,0.00023741921],"about_ca_topic_score_codex":0.00005346447,"about_ca_topic_score_gemma":0.000093625546,"teacher_disagreement_score":0.84575653,"about_ca_system_score_codex":0.000068737616,"about_ca_system_score_gemma":0.000045662637,"threshold_uncertainty_score":0.9997695},"labels":[],"label_agreement":null},{"id":"W1998617839","doi":"10.1287/inte.1080.0344","title":"A Spreadsheet Implementation of an Ammunition Requirements Planning Model for the Canadian Army","year":2008,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Spreadsheets and End-User Computing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Ammunition; Variety (cybernetics); Engineering; Operations research; Engineering management; Operations management; Aeronautics; Computer science; Artificial intelligence; Geography","score_opus":0.09789625560620263,"score_gpt":0.3324277299142118,"score_spread":0.2345314743080092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998617839","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21780759,0.0000142382805,0.7782573,0.00031738015,0.0002114436,0.0003223354,0.000012416179,0.00003742015,0.0030198554],"genre_scores_gemma":[0.9772693,0.00001468801,0.021795651,0.0007639488,0.00010707601,0.0000078930125,0.000012384336,0.0000103868715,0.000018692319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986315,0.000008198089,0.00048271343,0.00012388598,0.00040211345,0.00035161167],"domain_scores_gemma":[0.9988977,0.000067245135,0.00034330072,0.00033656158,0.00016023971,0.0001950014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055757863,0.00013709025,0.00015609659,0.00027372385,0.0008700306,0.00019886681,0.00072150375,0.000051327734,0.000006086393],"category_scores_gemma":[0.00001120016,0.000096028816,0.00007975456,0.0002406946,0.00003923713,0.0005147367,0.00005519946,0.00022594578,0.0000029665073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040937346,0.000049890754,0.0009317601,0.000014045676,0.00013194229,0.000013867802,0.008472201,0.8798738,0.00015551044,0.044862878,0.0011004932,0.06435265],"study_design_scores_gemma":[0.0006889465,0.00014251287,0.002267541,0.000022258359,0.000020349013,0.000061082166,0.0005342756,0.9910951,0.0006273803,0.00368309,0.0007146941,0.00014273736],"about_ca_topic_score_codex":0.0016551944,"about_ca_topic_score_gemma":0.0031542438,"teacher_disagreement_score":0.7594617,"about_ca_system_score_codex":0.00018801675,"about_ca_system_score_gemma":0.0004263166,"threshold_uncertainty_score":0.669166},"labels":[],"label_agreement":null},{"id":"W2002507421","doi":"10.1287/inte.1110.0611","title":"A Strategic Empty Container Logistics Optimization in a Major Shipping Company","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Operations research; Container (type theory); Stock (firearms); Safety stock; Business; Profit (economics); Service (business); Computer science; Operations management; Transport engineering; Marketing; Supply chain; Engineering; Economics","score_opus":0.04407673720646854,"score_gpt":0.28347552139311916,"score_spread":0.23939878418665061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002507421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02738197,0.00009523916,0.94137895,0.00004482422,0.00036150162,0.00018619622,0.000005028254,0.0001774625,0.03036885],"genre_scores_gemma":[0.92333275,0.00009894792,0.07598775,0.00022555352,0.00025486993,0.000004802322,0.0000132316145,0.000044131724,0.000037956157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982901,0.000025361958,0.00073535694,0.00009262938,0.0003179696,0.0005385445],"domain_scores_gemma":[0.9991875,0.00011915099,0.00018534753,0.00018429861,0.00008169624,0.0002419566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009814635,0.00024146202,0.0003294938,0.00040472977,0.00009578598,0.00016747731,0.0001885807,0.00016255057,0.00009989936],"category_scores_gemma":[0.00006568317,0.00021263507,0.00007041595,0.0005576549,0.00003655319,0.00026761644,0.000025046667,0.0007210419,0.00003919832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021892807,0.000037971575,0.0020296166,0.000026052023,0.000042661126,0.000005489,0.00026198363,0.98672605,0.00005303101,0.009557522,0.000057413974,0.0011803193],"study_design_scores_gemma":[0.00096242776,0.000027707198,0.00066565705,0.00005076745,0.000033766708,0.000045585588,0.0005013538,0.9964582,0.000100893805,0.000543193,0.00031486008,0.00029563627],"about_ca_topic_score_codex":0.0000013642212,"about_ca_topic_score_gemma":0.0000024794401,"teacher_disagreement_score":0.8959508,"about_ca_system_score_codex":0.00034560662,"about_ca_system_score_gemma":0.000047459667,"threshold_uncertainty_score":0.86710066},"labels":[],"label_agreement":null},{"id":"W2004985180","doi":"10.1287/inte.30.2.41.11673","title":"Air Transat Uses ALTITUDE to Manage Its Aircraft Routing, Crew Pairing, and Work Assignment","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Charter; Scheduling (production processes); Operations research; Crew scheduling; Crew; Flexibility (engineering); Navy; Work (physics); Computer science; Operations management; Engineering; Aeronautics","score_opus":0.01623516938582708,"score_gpt":0.24733184391631788,"score_spread":0.2310966745304908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004985180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3454668,0.0002047692,0.58566236,0.000805962,0.0003794413,0.0006921501,0.000011732758,0.000814939,0.065961815],"genre_scores_gemma":[0.9571364,0.000430669,0.039794795,0.0012600195,0.00019879367,0.000009022171,0.0000027378032,0.00007478044,0.0010927668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981722,0.000019603289,0.00065125467,0.00019216588,0.000461879,0.0005028818],"domain_scores_gemma":[0.9991293,0.000084321204,0.000090529116,0.00023952764,0.000042353633,0.00041398083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067209563,0.00032082715,0.00035241028,0.00026351545,0.00019741213,0.00021435373,0.0002563098,0.00013424671,0.00038357015],"category_scores_gemma":[0.000029383562,0.00028339773,0.00009911081,0.0004912704,0.000027023703,0.00018837175,0.00002582426,0.00056176574,0.00019076315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034410856,0.000022472766,0.00043332067,0.000030096753,0.000107129155,0.000017369279,0.0006073304,0.92543274,0.00006077011,0.0004144449,0.0005471307,0.07229279],"study_design_scores_gemma":[0.0057484144,0.0006291596,0.028043708,0.0012398897,0.00054038095,0.0002966562,0.00068238226,0.63751686,0.008345867,0.0007937312,0.31260198,0.0035609854],"about_ca_topic_score_codex":9.3708206e-7,"about_ca_topic_score_gemma":0.0000019295805,"teacher_disagreement_score":0.6116696,"about_ca_system_score_codex":0.00019365942,"about_ca_system_score_gemma":0.00001907077,"threshold_uncertainty_score":0.9999618},"labels":[],"label_agreement":null},{"id":"W2014291971","doi":"10.1287/inte.1090.0448","title":"Optimization Helps Shermag Gain Competitive Edge","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Supply chain optimization; Procurement; Competitive advantage; Supply chain network; Software; Component (thermodynamics); Computer science; Market share; Total cost; Operations research; Supply chain management; Engineering; Business; Marketing","score_opus":0.01019653572668703,"score_gpt":0.22307428505346844,"score_spread":0.2128777493267814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014291971","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013266561,0.00005582746,0.83792526,0.00025864333,0.00040326748,0.00011880915,0.000005675091,0.0003086486,0.15959723],"genre_scores_gemma":[0.81597763,0.00086227176,0.17875361,0.0030336606,0.0008484042,0.0000039555157,0.00006737091,0.00006297381,0.00039009214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890894,0.0000053167832,0.00041973614,0.000092950446,0.00030049164,0.0002725744],"domain_scores_gemma":[0.99942297,0.000031319752,0.000100204896,0.00014534079,0.000109129214,0.00019103158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019234708,0.0001972035,0.00021015659,0.00025038293,0.00012962753,0.0001949932,0.00016762032,0.00011046721,0.00016497006],"category_scores_gemma":[0.000023197494,0.0001703009,0.00008290696,0.00035773235,0.000023044291,0.00017655831,0.000007851688,0.00045983854,0.00012163323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014190088,0.000023913799,0.0000054838674,0.0000036579509,0.00003306729,0.000007771684,0.00014347804,0.9814441,0.000022389606,0.005742795,0.0005208128,0.012038318],"study_design_scores_gemma":[0.0006584092,0.00007233719,0.00006394774,0.00003346763,0.0000186624,0.000037552287,0.00023846098,0.995086,0.0006588679,0.00040806097,0.0024708393,0.00025340155],"about_ca_topic_score_codex":1.2009416e-7,"about_ca_topic_score_gemma":1.9357272e-7,"teacher_disagreement_score":0.814651,"about_ca_system_score_codex":0.000135854,"about_ca_system_score_gemma":0.0000303176,"threshold_uncertainty_score":0.6944669},"labels":[],"label_agreement":null},{"id":"W2015655819","doi":"10.1287/inte.1120.0632","title":"Introduction to the Special Issue on Analytics in Sports, Part II: Sports Scheduling Applications","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"League; Football; Scheduling (production processes); Analytics; Sport management; Recreation; Operations research; Computer science; Engineering; Advertising; Data science; Business; Political science; Public relations; Operations management","score_opus":0.05280231666078187,"score_gpt":0.34032686957096836,"score_spread":0.28752455291018647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015655819","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54823506,0.00030986432,0.09144304,0.09449985,0.018310254,0.0033793855,0.00008745565,0.00035945643,0.24337563],"genre_scores_gemma":[0.93268824,0.00010905995,0.0044851853,0.002208115,0.055839296,0.00003326179,0.0000136082635,0.000032463675,0.0045907553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.994532,0.00003824823,0.0015974877,0.0004021412,0.002629297,0.0008008169],"domain_scores_gemma":[0.9969658,0.00036559624,0.00072941167,0.0010355565,0.00036282404,0.00054075714],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.007576582,0.000308009,0.00047349586,0.0012815321,0.0009947052,0.00046225067,0.00084799074,0.0001662841,0.001090768],"category_scores_gemma":[0.0012127317,0.00019218076,0.00023207656,0.0032573303,0.00009940255,0.00039153532,0.00012669845,0.0011524025,0.0016238267],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021253472,0.0006846769,0.008204288,0.000004027089,0.000072033385,0.000008660581,0.0014938674,0.63039017,0.00001402704,0.07056223,0.12044005,0.16791345],"study_design_scores_gemma":[0.00031200785,0.00007418229,0.0055340957,0.000025458217,0.0000612679,0.000048391288,0.0014985593,0.0030721917,0.000112163965,0.0057787085,0.9832009,0.00028208503],"about_ca_topic_score_codex":0.0000027718888,"about_ca_topic_score_gemma":0.000012852975,"teacher_disagreement_score":0.86276084,"about_ca_system_score_codex":0.0002620844,"about_ca_system_score_gemma":0.00018766898,"threshold_uncertainty_score":0.9998224},"labels":[],"label_agreement":null},{"id":"W2015973458","doi":"10.1287/inte.32.2.74.64","title":"How Should Team Captains Order Golfers on the Final Day of the Ryder Cup Matches?","year":2002,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada; Royal Ottawa Mental Health Centre","funders":"","keywords":"Order (exchange); SLATES; Advertising; Momentum (technical analysis); Engineering; Management; Operations research; Operations management; Marketing; Business; Computer science; Economics; World Wide Web; Finance","score_opus":0.08696453909582184,"score_gpt":0.2210731652860867,"score_spread":0.13410862619026487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015973458","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43130437,0.0005263934,0.0033612868,0.03207587,0.0015923614,0.00083156175,0.00029224477,0.000050498154,0.5299654],"genre_scores_gemma":[0.98291546,0.0007043933,0.000052627624,0.003454773,0.00025464126,0.000005490461,0.0000021057385,0.000029170158,0.012581347],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982917,0.0000050109975,0.0008370791,0.00020689836,0.00024564497,0.0004136815],"domain_scores_gemma":[0.9979969,0.00013262662,0.0010328258,0.00062377035,0.000091754766,0.0001220702],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008495424,0.0002724425,0.00043476425,0.00024907946,0.00035704713,0.00031173532,0.00069710455,0.00014539358,0.0010978522],"category_scores_gemma":[0.00015952045,0.00015231797,0.00030629663,0.0005806223,0.00017553144,0.00017163997,0.00006660222,0.0008813984,0.0002488137],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063753454,0.0005413746,0.05400978,0.000073012525,0.00051181903,0.000011291392,0.0022442504,0.039296065,0.000017389279,0.8153696,0.082809255,0.0050523793],"study_design_scores_gemma":[0.0016781483,0.00039031022,0.025111329,0.00012547022,0.00009969623,0.000051040544,0.0015108596,0.15308154,0.0003739438,0.01663828,0.800036,0.0009033631],"about_ca_topic_score_codex":0.00001066076,"about_ca_topic_score_gemma":0.000009188402,"teacher_disagreement_score":0.7987313,"about_ca_system_score_codex":0.00014518647,"about_ca_system_score_gemma":0.000034986926,"threshold_uncertainty_score":0.9998153},"labels":[],"label_agreement":null},{"id":"W2019742604","doi":"10.1287/inte.30.1.96.11617","title":"An Asset and Liability Management System for Towers Perrin-Tillinghast","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian General-Tower (Canada)","funders":"","keywords":"Liability; Asset (computer security); Pension; Actuarial science; Business; Plan (archaeology); Investment (military); Asset management; Generator (circuit theory); Finance; Risk management; Risk analysis (engineering); Computer science; Power (physics); Computer security","score_opus":0.015810974103581617,"score_gpt":0.296733585176161,"score_spread":0.2809226110725794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019742604","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.510847,0.00005732666,0.005554805,0.0004945513,0.00066796475,0.0018299343,0.000052342333,0.00023230525,0.4802638],"genre_scores_gemma":[0.9952229,0.00064528396,0.0023509003,0.00044871122,0.00036684258,0.000034099437,0.000009370209,0.00002120986,0.0009006547],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99757516,0.000042909207,0.0006432223,0.00030451565,0.0008428292,0.0005913662],"domain_scores_gemma":[0.9987972,0.00006234474,0.00026353702,0.00036736354,0.00013056285,0.00037903816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025666878,0.00024173559,0.00033263038,0.00026237514,0.0010648605,0.0006084251,0.00047055306,0.00012293698,0.00014017522],"category_scores_gemma":[0.000014155642,0.00020377916,0.00018172081,0.0003785656,0.0002604961,0.0003555793,0.000027492113,0.00029073,0.00003729282],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005100308,0.0004704096,0.010068095,0.00040012778,0.00063673494,0.00005009267,0.0055791745,0.011703115,0.0000060109287,0.4416456,0.002259713,0.5266709],"study_design_scores_gemma":[0.0025040433,0.00046161975,0.018902805,0.00014509127,0.00039939483,0.000013680799,0.029679585,0.0058108848,0.000026319512,0.009184344,0.9319349,0.0009373584],"about_ca_topic_score_codex":0.00005875955,"about_ca_topic_score_gemma":0.00009603624,"teacher_disagreement_score":0.92967516,"about_ca_system_score_codex":0.0003194528,"about_ca_system_score_gemma":0.000056837394,"threshold_uncertainty_score":0.83098733},"labels":[],"label_agreement":null},{"id":"W2025926540","doi":"10.1287/inte.1100.0525","title":"A Quarter of a Century of Academia–Industry Interfacing: The Alabama Productivity Center","year":2010,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Outreach; Quarter (Canadian coin); Center (category theory); Productivity; Interfacing; Public relations; Service (business); Tertiary sector of the economy; Engineering management; Marketing; Management; Engineering; Political science; Business; Economics; Economic growth","score_opus":0.007039499123187908,"score_gpt":0.22362755919410926,"score_spread":0.21658806007092135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025926540","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9881427,0.000016398802,0.00041706144,0.0003350545,0.0008824889,0.00010954371,0.00000493116,0.00003573804,0.010056044],"genre_scores_gemma":[0.99932957,0.000025858388,0.00035043227,0.00006397373,0.0001541125,0.000004173943,0.000001572483,0.000014318779,0.000055986457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990414,0.0000032614823,0.00044877615,0.000066163106,0.00025782565,0.00018258081],"domain_scores_gemma":[0.9994575,0.000026131753,0.00014811104,0.00020144171,0.00007472469,0.00009208534],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00029104188,0.00013907765,0.00018232838,0.00013949518,0.00003380568,0.00002450806,0.00022536486,0.00022965069,0.00009455684],"category_scores_gemma":[0.000031162326,0.00008797195,0.00006187212,0.00022548797,0.000045601228,0.000060602408,0.000027112843,0.0023799313,0.0000117401405],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028502138,0.0019276451,0.03808808,0.0018494546,0.0030051218,0.00001522011,0.034553047,0.29962146,0.24433678,0.08092162,0.081232406,0.21416414],"study_design_scores_gemma":[0.004817442,0.0004761357,0.094549745,0.0009626601,0.0003049401,0.00077327085,0.009803873,0.031840008,0.4798091,0.0028864348,0.37143752,0.0023388776],"about_ca_topic_score_codex":4.76715e-7,"about_ca_topic_score_gemma":0.0000010380642,"teacher_disagreement_score":0.29020512,"about_ca_system_score_codex":0.000043008597,"about_ca_system_score_gemma":0.000050722807,"threshold_uncertainty_score":0.9999216},"labels":[],"label_agreement":null},{"id":"W2027991041","doi":"10.1287/inte.1110.0594","title":"Introduction: 2010 Daniel H. Wagner Prize for Excellence in Operations Research Practice","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Operations Management Techniques","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"IBM; Competition (biology); CLARITY; Excellence; Heuristics; Center of excellence; Operations research; Suite; Obsolescence; Watson; Operational excellence; Schedule; Computer science; Management; Marketing; Business; Engineering; Political science; Economics; Artificial intelligence","score_opus":0.29580409811915603,"score_gpt":0.45412619037885954,"score_spread":0.1583220922597035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027991041","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012532815,0.000059520768,0.43914115,0.043387666,0.0019719522,0.0032164315,0.000018060606,0.0001778708,0.49949452],"genre_scores_gemma":[0.69768816,0.00047971788,0.23902436,0.0026078755,0.0020253719,0.00029798836,0.000012360629,0.00005548651,0.05780867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99593776,0.000108721615,0.0012705061,0.00039621777,0.001843452,0.00044331257],"domain_scores_gemma":[0.9961348,0.00086454017,0.00023741502,0.0008279989,0.0017765083,0.0001587252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013791901,0.00017379197,0.00027094476,0.0016103984,0.00062423485,0.0010347515,0.0011309886,0.00012495322,0.0006715569],"category_scores_gemma":[0.0063597774,0.00012140857,0.00009370879,0.0019020499,0.0001750633,0.0017242852,0.00018624119,0.0009468839,0.00054948044],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043745668,0.0006996757,0.0001765001,0.000009202878,0.00006200684,0.000025761503,0.0028145402,0.010214738,0.00051759754,0.5377205,0.41171163,0.035610408],"study_design_scores_gemma":[0.000981213,0.0007135587,0.00061051425,0.000026933578,0.00003549016,0.000076626806,0.008454221,0.017634263,0.0025195822,0.06487746,0.9036801,0.00039002625],"about_ca_topic_score_codex":0.000024709721,"about_ca_topic_score_gemma":0.00009222595,"teacher_disagreement_score":0.68515533,"about_ca_system_score_codex":0.00021212256,"about_ca_system_score_gemma":0.00023124105,"threshold_uncertainty_score":0.9978132},"labels":[],"label_agreement":null},{"id":"W2046076181","doi":"10.1287/inte.1070.0322","title":"Optimizing Highway Transportation at the United States Postal Service","year":2007,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Postal service; Service (business); Transport engineering; Business; Finance; Engineering; Marketing","score_opus":0.019345960917214966,"score_gpt":0.2791708987249374,"score_spread":0.2598249378077224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046076181","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9046055,0.000030465939,0.0575869,0.004723721,0.0004392151,0.00030779128,0.00004503326,0.00018571346,0.032075662],"genre_scores_gemma":[0.99303263,0.00024684888,0.0015174755,0.0036313909,0.00019012438,0.0000016074446,0.00048036338,0.00001684162,0.0008827007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99843574,0.000015941952,0.00047604015,0.00010192325,0.00063033035,0.00034001825],"domain_scores_gemma":[0.9989095,0.00019973694,0.00032378014,0.000101788566,0.00027853416,0.0001867075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012244096,0.0001285124,0.000121047255,0.00023706123,0.00116649,0.00017032833,0.00020002661,0.000106420244,0.00012938639],"category_scores_gemma":[0.000020110734,0.000088889195,0.00006675732,0.0009191602,0.000092471164,0.00020691504,0.000002977166,0.00032843126,0.00004966971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028900141,0.00004080137,0.0013181574,0.000009521165,0.00006834588,0.000022272226,0.05017513,0.9192377,0.000049936953,0.02526229,0.0012786094,0.0022482611],"study_design_scores_gemma":[0.0034962925,0.00024535015,0.038986605,0.00016448692,0.0003611101,0.000021242366,0.121067815,0.018899025,0.0016384257,0.0028562436,0.8110922,0.0011712058],"about_ca_topic_score_codex":0.0002691842,"about_ca_topic_score_gemma":0.0026810102,"teacher_disagreement_score":0.90033865,"about_ca_system_score_codex":0.00017513285,"about_ca_system_score_gemma":0.00012000685,"threshold_uncertainty_score":0.89718163},"labels":[],"label_agreement":null},{"id":"W2053134203","doi":"10.1287/inte.1090.0445","title":"Introduction: Applications of Management Science and Operations Research Models and Methods to Problems in Health Care","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Health care; Management science; Queueing theory; Operations research; Health care delivery; Computer science; Affect (linguistics); Risk analysis (engineering); Engineering; Business; Economics; Psychology","score_opus":0.10820970897441509,"score_gpt":0.514470254749689,"score_spread":0.40626054577527393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053134203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019463778,0.0004325921,0.90396655,0.05091437,0.0001679174,0.0053414204,0.000012684297,0.000040651685,0.019660061],"genre_scores_gemma":[0.6564926,0.0013960203,0.33910125,0.0023244754,0.00023614334,0.00016836463,0.000009431919,0.000011168813,0.0002605289],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784076,0.00013260747,0.0008769047,0.0002369537,0.00051895995,0.0003938302],"domain_scores_gemma":[0.99821496,0.000075500924,0.000112414775,0.0002547164,0.0010321542,0.00031027646],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0066775605,0.00009691679,0.00022473736,0.0011308743,0.0016713658,0.00008499849,0.00015379014,0.00007229003,0.000011330617],"category_scores_gemma":[0.00011572931,0.00007869966,0.0000119646875,0.0018993003,0.00011213234,0.0002554953,0.000067940775,0.00073047617,0.0000051285806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023103727,0.00005666858,0.00007629335,0.00020384602,0.00000728084,2.7458523e-7,0.011598424,0.46781808,0.0000722069,0.35336015,0.00031979807,0.16646385],"study_design_scores_gemma":[0.004917852,0.0029016035,0.011257006,0.0016274397,0.000053124186,0.00004694315,0.15256761,0.635726,0.00024966354,0.045974493,0.14357221,0.0011060848],"about_ca_topic_score_codex":0.000043140924,"about_ca_topic_score_gemma":0.000086218715,"teacher_disagreement_score":0.6370288,"about_ca_system_score_codex":0.0005271317,"about_ca_system_score_gemma":0.0007959298,"threshold_uncertainty_score":0.9996283},"labels":[],"label_agreement":null},{"id":"W2056364446","doi":"10.1287/inte.30.3.95.11655","title":"Just-in-Time Manufacturing and Pollution Prevention Generate Mutual Benefits in the Furniture Industry","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Environmental Sustainability in Business","field":"Business, Management and Accounting","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Business; Control (management); Pollution prevention; Pollution; Production (economics); Investment (military); Work (physics); Environmental pollution; Manufacturing; Environmental economics; Industrial organization; Operations management; Marketing; Engineering; Environmental protection; Environmental science; Waste management; Economics; Management","score_opus":0.013739213167505173,"score_gpt":0.22032558360663326,"score_spread":0.2065863704391281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056364446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99268365,0.000023448394,0.000009703076,0.0010821602,0.000041726245,0.00024871834,0.000001219039,0.000015274032,0.0058940756],"genre_scores_gemma":[0.99629414,0.00006463724,0.000032853342,0.0027284136,0.00050205603,0.000007174143,0.0000131729585,0.000015005632,0.00034257155],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99863,0.000009213669,0.00045226997,0.00016069127,0.00042231003,0.0003255549],"domain_scores_gemma":[0.99958193,0.00001684802,0.0002001768,0.00016375951,0.00001709366,0.000020207546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074850774,0.00020112678,0.00018533967,0.00032377732,0.00018901375,0.00048226447,0.00026427713,0.00021136235,0.00037099828],"category_scores_gemma":[0.000017131346,0.00014176709,0.000049608774,0.0003679439,0.00006159659,0.00083677244,0.00006378858,0.0008930221,0.0001296472],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075939705,0.00091551244,0.066764355,0.00029986908,0.00008572984,0.00019068945,0.0009197897,0.45793307,0.0001926185,0.0092698075,0.0011843567,0.4614848],"study_design_scores_gemma":[0.0032452662,0.000054293494,0.9449134,0.00022532987,0.00011336407,0.00010927606,0.002139891,0.008423072,0.00025074132,0.009363357,0.030434977,0.0007270246],"about_ca_topic_score_codex":0.000022023238,"about_ca_topic_score_gemma":0.000029318304,"teacher_disagreement_score":0.87814903,"about_ca_system_score_codex":0.00018710447,"about_ca_system_score_gemma":0.000016218224,"threshold_uncertainty_score":0.5781094},"labels":[],"label_agreement":null},{"id":"W2074009058","doi":"10.1287/inte.32.2.28.59","title":"In Search of Strategic Operations Research/Management Science","year":2002,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Competitive advantage; Strategic planning; Work (physics); Strategic management; Key (lock); Inclusion (mineral); Profit impact of marketing strategy; Business; Strategic information system; Strategic thinking; Strategic financial management; Knowledge management; Process management; Information system; Computer science; Engineering; Marketing; Management information systems; Chemistry","score_opus":0.25511617382513846,"score_gpt":0.37613531622006535,"score_spread":0.12101914239492689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074009058","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42694318,0.00004052534,0.0007779034,0.0012100867,0.0003117485,0.0003591853,0.0000033790466,0.00002488475,0.57032907],"genre_scores_gemma":[0.99817425,0.00018649858,0.000292538,0.0005354256,0.00029817937,0.000004486029,0.0000028479783,0.00000986057,0.00049593695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975491,0.0000039607003,0.0005255761,0.00019212987,0.0012468961,0.0004822956],"domain_scores_gemma":[0.9990682,0.000026301816,0.00008989808,0.0003164034,0.00046556318,0.00003362075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001977991,0.00012852302,0.00017627976,0.002008506,0.0003843259,0.0007523641,0.0008395919,0.000049111863,0.000713605],"category_scores_gemma":[0.000050858092,0.00009725641,0.000042184525,0.0035795434,0.0004131616,0.0016283884,0.00024605507,0.00056845107,0.0007005179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030585456,0.0002821795,0.00030523396,0.00010461988,0.000018307566,0.00003661319,0.00009575113,0.0242259,0.0005382042,0.96226245,0.0014544678,0.010645694],"study_design_scores_gemma":[0.0033151186,0.00020196493,0.008241659,0.00083576824,0.000105038365,0.00007298276,0.0116564585,0.7841619,0.0036323194,0.09372948,0.092575945,0.0014713687],"about_ca_topic_score_codex":0.00006161637,"about_ca_topic_score_gemma":0.000038572594,"teacher_disagreement_score":0.86853296,"about_ca_system_score_codex":0.00010520659,"about_ca_system_score_gemma":0.00004509882,"threshold_uncertainty_score":0.90039736},"labels":[],"label_agreement":null},{"id":"W2083945967","doi":"10.1287/inte.1090.0444","title":"Rebuttal of “Polar Bear Population Forecasts: A Public-Policy Forecasting Audit”","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Office of Science; U.S. Forest Service; U.S. Geological Survey; Woods Hole Oceanographic Institution; Arctic Institute of North America; U.S. Department of Energy; National Science Foundation","keywords":"Population; Listing (finance); Audit; Rebuttal; Endangered species; Government (linguistics); Threatened species; Geography; Operations research; Political science; Business; Accounting; Ecology; Engineering; Habitat; Sociology; Finance; Law; Biology; Demography","score_opus":0.022039232329443968,"score_gpt":0.23342010357367826,"score_spread":0.2113808712442343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083945967","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92719346,0.00002828635,0.0063021095,0.001641874,0.0002465933,0.00021519992,0.000079847516,0.00004976486,0.06424286],"genre_scores_gemma":[0.9950178,0.00007125036,0.0032519307,0.00096405944,0.00047452693,1.4440853e-7,0.00011221306,0.0000054438515,0.00010262671],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812603,0.000012723209,0.0006806881,0.00013970182,0.0005726565,0.00046819518],"domain_scores_gemma":[0.9987726,0.0000921345,0.0005823093,0.0001767804,0.0001097562,0.00026637988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005322955,0.00019838187,0.00030135814,0.00041273888,0.0003185396,0.00013694433,0.0002753624,0.00009918021,0.00024562288],"category_scores_gemma":[0.00018507529,0.00014674214,0.00015259546,0.00054718874,0.00006751362,0.00045480308,0.000011002873,0.00047236486,0.000045028606],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026535697,0.00009631077,0.10169344,0.00004075573,0.00013187919,0.00005041986,0.00059823797,0.037603535,0.000020556432,0.035772774,0.00044476922,0.82328194],"study_design_scores_gemma":[0.002548346,0.0022481417,0.37039495,0.00021937776,0.00014322982,0.0013707192,0.002321406,0.5039005,0.000083311446,0.10558739,0.010048445,0.0011341682],"about_ca_topic_score_codex":0.00014992761,"about_ca_topic_score_gemma":0.00007474282,"teacher_disagreement_score":0.8221478,"about_ca_system_score_codex":0.000046495406,"about_ca_system_score_gemma":0.00018763245,"threshold_uncertainty_score":0.5983971},"labels":[],"label_agreement":null},{"id":"W2094427046","doi":"10.1287/inte.1070.0337","title":"Spreadsheet Model Helps to Assign Medical Residents at the University of Vermont's College of Medicine","year":2008,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Rotman School of Management, University of Toronto","keywords":"Scope (computer science); Scheduling (production processes); Computer science; Operations research; Software; Engineering management; Software engineering; Operations management; Engineering; Programming language","score_opus":0.11200069251111452,"score_gpt":0.3425552182805954,"score_spread":0.23055452576948085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094427046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7997708,0.00014174367,0.11261544,0.01405232,0.0005043216,0.00036739136,0.00017210974,0.00003653323,0.07233939],"genre_scores_gemma":[0.99258393,0.00014648776,0.001346375,0.0005656031,0.000079381985,2.1447892e-7,0.0000016556371,0.0000065621825,0.0052697756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99544394,0.000043729153,0.0008596446,0.00016775134,0.0032366926,0.00024825794],"domain_scores_gemma":[0.9971145,0.0009128871,0.0005803638,0.00049929606,0.0005019848,0.00039101345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003647815,0.00012988567,0.00044978486,0.0004374246,0.00050677045,0.0000111422505,0.0011083311,0.000120550176,0.00057514507],"category_scores_gemma":[0.0022865033,0.00007238093,0.00015906608,0.0011465163,0.00058391155,0.00010329696,0.0001816414,0.00037331265,0.00015618962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013834896,0.00026954393,0.0047558444,0.0000107563665,0.00032301722,0.00017841998,0.0039446037,0.47790298,0.00063980586,0.030379847,0.47533226,0.0048794267],"study_design_scores_gemma":[0.01681441,0.002201421,0.06826694,0.00090819097,0.0006888191,0.0015301178,0.016611086,0.68514913,0.00529411,0.115604736,0.0855213,0.0014097517],"about_ca_topic_score_codex":0.000036648573,"about_ca_topic_score_gemma":0.0000625197,"teacher_disagreement_score":0.38981095,"about_ca_system_score_codex":0.00011259639,"about_ca_system_score_gemma":0.00047815233,"threshold_uncertainty_score":0.6297435},"labels":[],"label_agreement":null},{"id":"W2098734012","doi":"10.1287/inte.32.2.42.66","title":"Student Consulting Projects Benefit Faculty and Industry","year":2002,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Spreadsheets and End-User Computing","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Engineering management; Engineering; Business; Knowledge management; Process management; Computer science","score_opus":0.055118753340031704,"score_gpt":0.2858808689916905,"score_spread":0.23076211565165883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098734012","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92358553,0.00009147463,0.016293483,0.0008362917,0.00040169503,0.00022873822,0.0000031118316,0.00018113782,0.058378555],"genre_scores_gemma":[0.9906501,0.000041361276,0.007756447,0.001053502,0.00019892401,0.0000021463723,8.063077e-7,0.000010957476,0.0002857475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832904,0.000005666116,0.00047853214,0.00023311876,0.000531989,0.0004216546],"domain_scores_gemma":[0.9989858,0.000071114,0.00030315242,0.00027786652,0.000100093464,0.00026196628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003496954,0.00021244813,0.00024007632,0.00020938384,0.00035896516,0.00084414764,0.00060954655,0.00015639944,0.000024523728],"category_scores_gemma":[0.000032846136,0.00015567808,0.00006025614,0.00038685364,0.00004042401,0.00035080258,0.0002900755,0.0009729506,0.000049292605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023417268,0.00042501348,0.030252026,0.000053196727,0.0004010026,0.00027151455,0.013808832,0.027100911,0.00015366063,0.20774409,0.005283999,0.7144823],"study_design_scores_gemma":[0.0061514573,0.00076960766,0.05261527,0.00043793986,0.00010939983,0.0018107956,0.0038652197,0.8803703,0.0012540196,0.0058859247,0.04473201,0.0019980585],"about_ca_topic_score_codex":0.0000031726167,"about_ca_topic_score_gemma":0.0000011907948,"teacher_disagreement_score":0.8532694,"about_ca_system_score_codex":0.00008504472,"about_ca_system_score_gemma":0.000029268691,"threshold_uncertainty_score":0.8140135},"labels":[],"label_agreement":null},{"id":"W2099087479","doi":"10.1287/inte.33.4.15.16372","title":"Early Detection of High-Risk Claims at the Workers' Compensation Board of British Columbia","year":2003,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Medical Malpractice and Liability Issues","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; Workers Compensation Board of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Mitacs","keywords":"Workers' compensation; Compensation (psychology); Business; Actuarial science; Engineering; Psychology","score_opus":0.023769102386332133,"score_gpt":0.31946593347660296,"score_spread":0.29569683109027084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099087479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98905534,0.00002089463,0.0011335356,0.00026317014,0.0004216732,0.0003835717,0.000018594954,0.000014567527,0.008688685],"genre_scores_gemma":[0.9969095,0.00021347906,0.0002535599,0.00038406294,0.00015140102,0.000010182665,0.000003677092,0.000013373859,0.0020607908],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975368,0.00022329725,0.0011515389,0.000117273055,0.000668823,0.00030227908],"domain_scores_gemma":[0.99655235,0.0011546593,0.0015050889,0.0002991974,0.00032373026,0.00016500309],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020510212,0.00009770473,0.00037606858,0.00006437511,0.00057496934,0.00004118291,0.00016943926,0.00022937327,0.0011300972],"category_scores_gemma":[0.00077163015,0.000087158274,0.00011920641,0.0003305755,0.00017671738,0.00013174865,0.0000387274,0.0012983447,0.000110535635],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014591808,0.0007590539,0.818224,0.00080320047,0.00092274277,0.000019996873,0.007337336,0.0023756255,0.0015357715,0.009075526,0.04616984,0.11131769],"study_design_scores_gemma":[0.005987754,0.0013306225,0.75852406,0.0009231894,0.0006828607,0.00004632483,0.024318019,0.00043975428,0.0013709696,0.026628016,0.17921068,0.00053777127],"about_ca_topic_score_codex":0.005998219,"about_ca_topic_score_gemma":0.010692475,"teacher_disagreement_score":0.13304083,"about_ca_system_score_codex":0.00017790573,"about_ca_system_score_gemma":0.00018811687,"threshold_uncertainty_score":0.999783},"labels":[],"label_agreement":null},{"id":"W2100639794","doi":"10.1287/inte.1050.0127","title":"A Florida County Locates Disaster Recovery Centers","year":2005,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Residence; Agency (philosophy); Emergency management; Mile; Transport engineering; Operations management; Engineering; Geography; Operations research; Business; Political science","score_opus":0.018808972725903362,"score_gpt":0.22096709783834945,"score_spread":0.20215812511244607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100639794","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3663842,0.00007582851,0.016907867,0.0119007975,0.0033982447,0.0007442852,0.000016891796,0.0003628819,0.600209],"genre_scores_gemma":[0.978202,0.00011326896,0.00021117704,0.017392341,0.002079235,0.000008364821,0.000037216098,0.000025698235,0.0019307119],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981791,0.000002340317,0.0006541819,0.0001932008,0.00056399516,0.00040719763],"domain_scores_gemma":[0.9992836,0.00001359149,0.00021458344,0.00028269473,0.0001572145,0.000048301474],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00047923662,0.00025423735,0.00023158912,0.00042395951,0.00026075303,0.00062075176,0.0003558032,0.000066270324,0.0009216066],"category_scores_gemma":[0.000040615716,0.00019918862,0.00016238622,0.00044068744,0.000050309885,0.0012325492,0.00010372136,0.000317949,0.0032917731],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081643555,0.0005950151,0.0041669756,0.00036816637,0.00058778503,0.000022753298,0.00031933776,0.37994352,0.0000996236,0.13548017,0.24471456,0.23288564],"study_design_scores_gemma":[0.0009822448,0.000023635885,0.0015729963,0.000046981095,0.00008777929,0.0000046812297,0.0007335852,0.042513262,0.00003402764,0.0019201454,0.9516521,0.0004285786],"about_ca_topic_score_codex":0.00003532246,"about_ca_topic_score_gemma":0.00010375729,"teacher_disagreement_score":0.7069375,"about_ca_system_score_codex":0.00017639896,"about_ca_system_score_gemma":0.000026694415,"threshold_uncertainty_score":0.9999917},"labels":[],"label_agreement":null},{"id":"W2101273286","doi":"10.1287/inte.1080.0405","title":"Fraser Health Uses Mathematical Programming to Plan Its Inpatient Hospital Network","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fraser Health; BC Cancer Agency","funders":"Fraser Health Authority","keywords":"Plan (archaeology); Process (computing); Population; Health care; Acute care; Operations management; Operations research; Capacity planning; Business; Computer science; Medicine; Engineering; Geography; Environmental health; Economics; Economic growth","score_opus":0.06127268751091639,"score_gpt":0.3935687067256145,"score_spread":0.3322960192146981,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101273286","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44747072,0.00018210612,0.41831908,0.080389805,0.0028278502,0.00872334,0.000058247213,0.0006154309,0.04141343],"genre_scores_gemma":[0.9112453,0.00013426576,0.05846583,0.028367788,0.0012648398,0.00005194217,0.000049560644,0.00003559225,0.0003849046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969595,0.0000718704,0.0013603502,0.00018947333,0.0005538082,0.0008649869],"domain_scores_gemma":[0.9980027,0.00014848943,0.00050123665,0.0002470371,0.00032299449,0.00077755615],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0013097955,0.00022772196,0.00042710776,0.000212462,0.0015365254,0.00012191836,0.00019861045,0.00019815112,0.00017776088],"category_scores_gemma":[0.000221208,0.00016682348,0.0000792311,0.000544411,0.000014976395,0.00016734816,0.000036310816,0.0011803621,0.00052608014],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004455799,0.0011737517,0.00578546,0.00036993742,0.00016401139,0.000036266818,0.021662991,0.36659777,0.000006127827,0.30010644,0.057548523,0.24610314],"study_design_scores_gemma":[0.010606251,0.0190361,0.025440726,0.007243426,0.00020942908,0.000062092506,0.029473398,0.15605429,0.00006520715,0.027992968,0.7199036,0.0039125257],"about_ca_topic_score_codex":0.0000031857014,"about_ca_topic_score_gemma":0.000011901723,"teacher_disagreement_score":0.66235507,"about_ca_system_score_codex":0.000417077,"about_ca_system_score_gemma":0.0007199388,"threshold_uncertainty_score":0.9997633},"labels":[],"label_agreement":null},{"id":"W2106360850","doi":"10.1287/inte.1120.0650","title":"Ford Motor Company Implements Integrated Planning and Scheduling in a Complex Automotive Manufacturing Environment","year":2012,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automotive industry; Scheduling (production processes); Stamping; Manufacturing engineering; Production planning; Supply chain; Engineering; Overtime; Industrial engineering; Operations research; Operations management; Business; Production (economics); Marketing; Economics","score_opus":0.027610855958282308,"score_gpt":0.2549771217926849,"score_spread":0.2273662658344026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106360850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8591964,0.00012555315,0.13787173,0.00003421175,0.00018588082,0.00020740858,0.00001383438,0.00012386609,0.0022410874],"genre_scores_gemma":[0.9467006,0.000076620425,0.052874986,0.00015476474,0.00011723471,0.000005722079,0.000024756755,0.00002997755,0.000015354684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986708,0.00000756525,0.0005073972,0.00009764588,0.00025254636,0.0004640469],"domain_scores_gemma":[0.99946904,0.000047321606,0.00011674495,0.00010472325,0.000017288281,0.00024486985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033789998,0.00023192645,0.0002663757,0.0003458646,0.0001176052,0.000116989104,0.000116410585,0.00008434563,0.00008997476],"category_scores_gemma":[0.000012685361,0.00019744919,0.000046318793,0.00011337065,0.000028394807,0.00022140845,0.00003808687,0.0005691204,0.000028111002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027925735,0.00003533755,0.0036137898,0.000019008676,0.00009095076,0.000006031373,0.0009268455,0.9862174,0.00012654364,0.0001801368,0.00003189201,0.00872416],"study_design_scores_gemma":[0.0015012379,0.00005305283,0.019542972,0.00008473595,0.000031084208,0.000047644695,0.0023932762,0.97175395,0.0018742683,0.00012410605,0.0022168078,0.00037686038],"about_ca_topic_score_codex":0.000001773048,"about_ca_topic_score_gemma":4.96695e-7,"teacher_disagreement_score":0.087504156,"about_ca_system_score_codex":0.0002650213,"about_ca_system_score_gemma":0.000012179293,"threshold_uncertainty_score":0.8051744},"labels":[],"label_agreement":null},{"id":"W2109092812","doi":"10.1287/inte.1050.0194","title":"The University of Toronto’s Rotman School of Management Uses Management Science to Create MBA Study Groups","year":2006,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Management and Marketing Education","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Work (physics); Group (periodic table); Working group; Process (computing); Mathematics education; Engineering management; Computer science; Management; Engineering; Psychology","score_opus":0.008498257883819513,"score_gpt":0.2199077129801811,"score_spread":0.2114094550963616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109092812","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37877008,0.000019048404,0.00094054476,0.00022704212,0.00033399856,0.00096070365,8.122094e-7,0.000039788763,0.61870795],"genre_scores_gemma":[0.9954879,0.00010143676,0.000502016,0.00021288605,0.0002216175,0.0000037340437,0.0000028107072,0.000012950594,0.0034546396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9980519,0.000006613614,0.0005120002,0.0002132142,0.0008750855,0.00034118746],"domain_scores_gemma":[0.99865335,0.000031132506,0.0006215415,0.00042944562,0.00022399852,0.000040507774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018452816,0.0001837697,0.0002136378,0.00040844423,0.00063857017,0.0003657496,0.00089506374,0.000024185541,0.00019003238],"category_scores_gemma":[0.000025606141,0.00013770601,0.00008787666,0.0007527009,0.00012689292,0.00067478995,0.00042102282,0.00011790527,0.00009626538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011641341,0.0015106143,0.05157425,0.0006616278,0.0007353166,0.00004407206,0.0004256593,0.012086815,0.00011749702,0.80222195,0.067906536,0.061551537],"study_design_scores_gemma":[0.0026613518,0.0001730311,0.66958755,0.000230167,0.00077969505,0.000001583193,0.030726437,0.001507587,0.000058106867,0.0068667307,0.28679886,0.00060889364],"about_ca_topic_score_codex":0.0005069101,"about_ca_topic_score_gemma":0.0004714767,"teacher_disagreement_score":0.7953552,"about_ca_system_score_codex":0.0002781596,"about_ca_system_score_gemma":0.000028505905,"threshold_uncertainty_score":0.5615488},"labels":[],"label_agreement":null},{"id":"W2113108023","doi":"10.1287/inte.32.4.28.54","title":"Implementing a Distribution-Network Decision-Support System at Pfizer/Warner-Lambert","year":2002,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Decision support system; Plan (archaeology); Operations research; Supply chain; Computer science; Operations management; Distribution (mathematics); Process management; Business; Engineering; Marketing; Artificial intelligence; Mathematics","score_opus":0.04395180645594679,"score_gpt":0.26697503864329386,"score_spread":0.22302323218734707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113108023","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08287508,0.0003168141,0.17259961,0.0017288674,0.0050926,0.0012304878,0.0001692644,0.0008502029,0.73513705],"genre_scores_gemma":[0.9914237,0.00012342085,0.00055630953,0.0019777832,0.003891205,0.000013224937,0.00024136895,0.00004830972,0.0017246536],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996466,0.000003858052,0.0011899668,0.00032091868,0.000983618,0.0010356682],"domain_scores_gemma":[0.998029,0.00010729541,0.0009671338,0.0004842669,0.00032968656,0.00008265002],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0011059535,0.00039681533,0.00046015106,0.0002573237,0.0011833376,0.0010798699,0.00067920884,0.00015713133,0.0027493036],"category_scores_gemma":[0.00011056093,0.000297728,0.00023544986,0.0011162355,0.00007325602,0.0010571235,0.0005315406,0.00050616555,0.004875455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024627187,0.00018473684,0.008533401,0.00027763692,0.00027559628,0.00018457294,0.000053274955,0.0075157695,0.00002168091,0.19066179,0.62988514,0.16216014],"study_design_scores_gemma":[0.0006724865,0.000018977178,0.0007005359,0.00019606341,0.00013917632,0.0001566739,0.00019166163,0.034978915,0.000026974845,0.0013926706,0.9610475,0.00047840647],"about_ca_topic_score_codex":0.000015557107,"about_ca_topic_score_gemma":0.000030091212,"teacher_disagreement_score":0.90854865,"about_ca_system_score_codex":0.0003188269,"about_ca_system_score_gemma":0.000025633213,"threshold_uncertainty_score":0.9999571},"labels":[],"label_agreement":null},{"id":"W2118867130","doi":"10.1287/inte.1050.0154","title":"Scheduling Employees in Quebec’s Liquor Stores with Integer Programming","year":2005,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Corporation; Scheduling (production processes); Integer programming; Schedule; Operations research; Computer science; Operations management; Business; Database; Engineering; Operating system; Finance","score_opus":0.06282176912898277,"score_gpt":0.35011510874133606,"score_spread":0.28729333961235326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118867130","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91881835,0.00016303988,0.06492106,0.003156189,0.00029267717,0.0003083823,0.0000038299486,0.00013615217,0.012200315],"genre_scores_gemma":[0.9527654,0.00002157139,0.044005483,0.00056622375,0.0004146681,0.000009457419,0.0000020796917,0.00002418027,0.002190942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99609095,0.000033940225,0.0012120534,0.00033154042,0.0016936241,0.0006379015],"domain_scores_gemma":[0.99767613,0.000573519,0.0005748761,0.0004601492,0.0003923207,0.0003230275],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0031801,0.00027564447,0.00046770225,0.0012823576,0.00034945796,0.0012734355,0.0007605846,0.00014068154,0.00012495121],"category_scores_gemma":[0.001083889,0.00016364349,0.00016878234,0.0016744854,0.00015690667,0.00068003987,0.00007047864,0.0011007371,0.00046636406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038397862,0.00044361386,0.01358321,0.000006046897,0.00014031226,0.000048035934,0.0023146432,0.649346,0.000049007394,0.023721661,0.0010998376,0.30886367],"study_design_scores_gemma":[0.00880402,0.0017282836,0.015326518,0.0009384746,0.00029089316,0.00050705636,0.02464764,0.17602873,0.0021794417,0.022292627,0.74435985,0.0028964859],"about_ca_topic_score_codex":0.0001211154,"about_ca_topic_score_gemma":0.0029847538,"teacher_disagreement_score":0.74325997,"about_ca_system_score_codex":0.00028426916,"about_ca_system_score_gemma":0.00042554116,"threshold_uncertainty_score":0.9997633},"labels":[],"label_agreement":null},{"id":"W2119849184","doi":"10.1287/inte.30.2.54.11677","title":"TransAlta Redesigns Its Service-Delivery Network","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"TransAlta (Canada); University of Alberta","funders":"","keywords":"Staffing; Service (business); Heuristics; Service delivery framework; Operations management; Operations research; Business; Engineering; Transport engineering; Computer science; Marketing","score_opus":0.022148449344022318,"score_gpt":0.24065371943035052,"score_spread":0.2185052700863282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119849184","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14503306,0.0004234153,0.390794,0.0009649975,0.0012191938,0.00074823794,0.00002823216,0.0018190851,0.45896974],"genre_scores_gemma":[0.92045987,0.002082974,0.070739806,0.0034634287,0.0015268616,0.000009991961,0.00001964065,0.00018897043,0.0015084505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983399,0.000018094124,0.0006378893,0.000122939,0.0003854844,0.0004956871],"domain_scores_gemma":[0.9992336,0.00008864212,0.00009077146,0.00023692496,0.000094348405,0.0002556833],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006004925,0.0002568846,0.0003006789,0.00014350118,0.00019401264,0.00017654791,0.0003164244,0.00017056064,0.0011816017],"category_scores_gemma":[0.000009306093,0.00022877268,0.00011110767,0.00073274085,0.000015597469,0.00020951316,0.000009100146,0.0007032373,0.0005058047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039743514,0.000012343885,0.000015012211,0.00002205936,0.00008398601,0.000009969509,0.00015524201,0.9525094,0.00012489031,0.0005766593,0.0020166526,0.044434056],"study_design_scores_gemma":[0.00084719795,0.00005547588,0.00011133521,0.00007637664,0.00006832759,0.000073239076,0.00005796552,0.9488401,0.0007041418,0.0005814772,0.048156578,0.00042778486],"about_ca_topic_score_codex":5.810564e-7,"about_ca_topic_score_gemma":0.0000016823277,"teacher_disagreement_score":0.7754268,"about_ca_system_score_codex":0.00012249271,"about_ca_system_score_gemma":0.00004942031,"threshold_uncertainty_score":0.9997315},"labels":[],"label_agreement":null},{"id":"W2120218454","doi":"10.1287/inte.1100.0510","title":"Approximate Dynamic Programming Captures Fleet Operations for Schneider National","year":2010,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Operations research; Fleet management; Quality (philosophy); Service (business); Dynamic programming; Computer science; Operations management; Engineering; Transport engineering; Business; Marketing","score_opus":0.012657447136643293,"score_gpt":0.2662540256432108,"score_spread":0.2535965785065675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120218454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27109236,0.000024633011,0.6926418,0.00188935,0.0019272906,0.0017441346,0.0002686483,0.0008981974,0.029513584],"genre_scores_gemma":[0.96972644,0.000008442828,0.029377824,0.00037256684,0.00014201063,0.00008143579,0.00013813764,0.000027611464,0.0001255152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890935,0.0000011559007,0.00047639824,0.00009031346,0.00029369222,0.00022908094],"domain_scores_gemma":[0.9993832,0.00003114096,0.000055095665,0.00011932246,0.000307842,0.0001033968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027712993,0.00015136866,0.00013702575,0.00026102155,0.00023978346,0.00020537004,0.00014016198,0.000110467496,0.000057691243],"category_scores_gemma":[0.000044387234,0.00012883877,0.000088232344,0.00026892943,0.000035281402,0.00020208396,0.000004005203,0.0005455552,0.00002015576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002206578,0.000093046496,0.00008107168,0.000058469064,0.00018286542,0.0000013864027,0.00048257824,0.6132335,0.008774255,0.359936,0.0014275009,0.015707241],"study_design_scores_gemma":[0.0025794175,0.000101664395,0.0027833965,0.000037834678,0.00012504034,0.00008650754,0.0010056295,0.7236596,0.0034446383,0.012734207,0.25255576,0.0008863615],"about_ca_topic_score_codex":6.783114e-7,"about_ca_topic_score_gemma":0.00013055866,"teacher_disagreement_score":0.6986341,"about_ca_system_score_codex":0.00006909333,"about_ca_system_score_gemma":0.000095743395,"threshold_uncertainty_score":0.5253893},"labels":[],"label_agreement":null},{"id":"W2124194632","doi":"10.1287/inte.1100.0550","title":"A Nonhomogeneous Agent-Based Simulation Approach to Modeling the Spread of Disease in a Pandemic Outbreak","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Outbreak; Pandemic; Disease; Transmission (telecommunications); Promotion (chess); Population; Agency (philosophy); Operations research; Computer science; Geography; Environmental health; Risk analysis (engineering); Business; Coronavirus disease 2019 (COVID-19); Medicine; Infectious disease (medical specialty); Virology; Engineering; Telecommunications; Political science","score_opus":0.2822713010076148,"score_gpt":0.38235906921112695,"score_spread":0.10008776820351212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124194632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4657432,0.000030379446,0.52865595,0.00014830942,0.000045195447,0.0006352198,0.000011609353,0.00004683835,0.0046833185],"genre_scores_gemma":[0.991683,0.000020650337,0.006912667,0.0012611031,0.000057840163,0.000024410858,0.0000020667421,0.000018754668,0.000019500383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979507,0.00004263686,0.0010275801,0.00018953657,0.0004411489,0.0003484097],"domain_scores_gemma":[0.99790496,0.00096448377,0.00043024102,0.00036368865,0.00011998828,0.00021665833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014118184,0.00023542141,0.0004889134,0.0002285097,0.00012491523,0.00002505287,0.00038070534,0.00009328753,0.000016282522],"category_scores_gemma":[0.0015439955,0.00013195202,0.00020400295,0.00037785413,0.000061317965,0.00005041029,0.000103639184,0.0004144328,0.000014452393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005394289,0.000275642,0.0060450896,0.00006245065,0.00006395637,0.0000053670788,0.0010118838,0.98267376,0.0000102486065,0.0071571497,0.000032860244,0.002122177],"study_design_scores_gemma":[0.00065451063,0.00010171723,0.0017178037,0.00007423638,0.00010658191,0.0000018003587,0.000284808,0.93611616,0.000019656898,0.060593426,0.00012522435,0.00020407152],"about_ca_topic_score_codex":0.00003514251,"about_ca_topic_score_gemma":0.000019127154,"teacher_disagreement_score":0.5259398,"about_ca_system_score_codex":0.00021505273,"about_ca_system_score_gemma":0.00009735503,"threshold_uncertainty_score":0.53808475},"labels":[],"label_agreement":null},{"id":"W2131427758","doi":"10.1287/inte.1090.0435","title":"A Simulation Model to Compare Strategies for the Reduction of Health-Care–Associated Infections","year":2009,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Isolation (microbiology); Hygiene; Infection control; Health care; Discrete event simulation; Medicine; Operations management; Medical emergency; Business; Operations research; Computer science; Intensive care medicine; Economics; Engineering; Simulation; Economic growth","score_opus":0.12066629367792,"score_gpt":0.45519526300400587,"score_spread":0.3345289693260859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131427758","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036850173,0.000040481787,0.9522729,0.0065910704,0.0003132798,0.0015813032,0.000036037836,0.000055700064,0.0022590363],"genre_scores_gemma":[0.9915964,0.000048802536,0.0055114725,0.002465252,0.00017511839,0.00004033849,0.000054787586,0.000012651027,0.00009516003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812686,0.000062639796,0.0010995762,0.00010978979,0.00028119216,0.0003199247],"domain_scores_gemma":[0.99766594,0.00033714387,0.00067170506,0.00018884795,0.0010032728,0.00013305889],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011088648,0.00013120516,0.0002883987,0.00024127828,0.0020107676,0.00006180516,0.000115546594,0.00009859519,0.000014523857],"category_scores_gemma":[0.00024802578,0.00008953694,0.00008983925,0.00047347735,0.000019673938,0.00018602234,0.000010253487,0.0005317412,0.000009329862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006758179,0.000054862037,0.00004561814,0.000026172924,0.000024489202,2.8503482e-8,0.006403893,0.96043026,0.000015993732,0.024640102,0.0007471113,0.007543867],"study_design_scores_gemma":[0.0007364584,0.00040458742,0.00079451455,0.0001303515,0.000031905074,6.8478533e-7,0.015484696,0.97727734,0.000005233661,0.0033273983,0.001690481,0.0001163516],"about_ca_topic_score_codex":0.00002598232,"about_ca_topic_score_gemma":0.00007411385,"teacher_disagreement_score":0.95474625,"about_ca_system_score_codex":0.00046932514,"about_ca_system_score_gemma":0.0013222761,"threshold_uncertainty_score":0.9992885},"labels":[],"label_agreement":null},{"id":"W2134421244","doi":"10.1287/inte.1040.0067","title":"General Motors Optimizes Its Scheduling of Cold-Weather Tests","year":2004,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"Oregon State University","keywords":"Warranty; Scheduling (production processes); Schedule; Operations research; Cold weather; Engineering; General motors; Computer science; Automotive engineering; Transport engineering; Operations management; Operating system","score_opus":0.023779067967838047,"score_gpt":0.28469505830260505,"score_spread":0.260915990334767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134421244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34937203,0.00013582807,0.6435668,0.0007976044,0.00016962882,0.00023299099,0.0000040319933,0.0001061368,0.005614983],"genre_scores_gemma":[0.90811974,0.00022923005,0.09091591,0.00032644573,0.00014408909,0.0000042256747,0.0000010842882,0.000013401404,0.0002458871],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976215,0.000016283795,0.00069576665,0.00023065726,0.0010030817,0.00043275513],"domain_scores_gemma":[0.9984209,0.00013829411,0.00033350557,0.0004695075,0.00035005296,0.00028775365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008424954,0.00020240547,0.00039039578,0.00047310695,0.00019534607,0.00030204156,0.0010776003,0.00012128168,0.000028224149],"category_scores_gemma":[0.00019460698,0.00014842616,0.00027145082,0.0010166955,0.000086393324,0.0004087095,0.00017123445,0.0005672807,0.00009166482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029765199,0.00021762389,0.00045265097,0.000028300621,0.00018736585,0.000027433194,0.0002750313,0.8418433,0.0021578623,0.14674613,0.000040164596,0.007994373],"study_design_scores_gemma":[0.008619471,0.0015885769,0.0024387292,0.00052210793,0.0002213562,0.0002586461,0.0004397042,0.7559995,0.15891665,0.060704965,0.008351039,0.0019392676],"about_ca_topic_score_codex":0.0000066882512,"about_ca_topic_score_gemma":0.0000016039063,"teacher_disagreement_score":0.5587477,"about_ca_system_score_codex":0.00019901573,"about_ca_system_score_gemma":0.00035279995,"threshold_uncertainty_score":0.60526437},"labels":[],"label_agreement":null},{"id":"W2139457845","doi":"10.1287/inte.1040.0097","title":"Improving Volunteer Scheduling for the Edmonton Folk Festival","year":2004,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Crew; Entertainment; Advertising; Scheduling (production processes); Operations management; Business; Operations research; Marketing; Psychology; Engineering; Aeronautics; Art; Visual arts","score_opus":0.08812373420240385,"score_gpt":0.35863292571073196,"score_spread":0.2705091915083281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139457845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04916585,0.00017709116,0.93892074,0.004243594,0.0012332847,0.00036553337,0.000013056061,0.00007516156,0.005805676],"genre_scores_gemma":[0.96329206,0.000027194541,0.033179376,0.0011014058,0.0009831865,0.000015376374,0.0000022566114,0.000026271993,0.0013728844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99641776,0.0000140870825,0.0010843718,0.00029579166,0.0015754071,0.00061257416],"domain_scores_gemma":[0.996137,0.0016656915,0.000714791,0.0005881665,0.0006200866,0.00027422357],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005136106,0.0002446452,0.00035299858,0.0004313235,0.0013770169,0.0012610875,0.001079975,0.00013854133,0.00004736632],"category_scores_gemma":[0.004260197,0.00012982074,0.0004170998,0.0009087439,0.00013809201,0.0003511576,0.00009073596,0.0007903745,0.00029297464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020753691,0.00011064497,0.00021099007,0.0000053425365,0.00018482695,0.000007651002,0.0005999362,0.79740214,0.00047418484,0.09525928,0.0014289015,0.10410857],"study_design_scores_gemma":[0.0074057174,0.0006713706,0.0019250397,0.00015449733,0.00056536024,0.00035200556,0.0076421937,0.4341283,0.0034922662,0.39848688,0.14397557,0.0012008039],"about_ca_topic_score_codex":0.00001852247,"about_ca_topic_score_gemma":0.000017778315,"teacher_disagreement_score":0.9141262,"about_ca_system_score_codex":0.00021044708,"about_ca_system_score_gemma":0.00047413944,"threshold_uncertainty_score":0.99992305},"labels":[],"label_agreement":null},{"id":"W2143105265","doi":"10.1287/inte.2013.0683","title":"Mathematical Programming Guides Air-Ambulance Routing at Ornge","year":2013,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Operations research; Work (physics); Computer science; Fixed wing; Routing (electronic design automation); Route planning; Air traffic control; Range (aeronautics); Vehicle routing problem; Operations management; Aeronautics; Transport engineering; Engineering; Computer network; Wing","score_opus":0.018716778310650772,"score_gpt":0.2584463641114855,"score_spread":0.23972958580083475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143105265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14036748,0.00004992605,0.7765049,0.0002687064,0.00031325134,0.00055898086,0.0000028665133,0.0008092486,0.08112467],"genre_scores_gemma":[0.7071429,0.000036093406,0.2917576,0.00032401364,0.00024787307,0.00001867343,0.0000043480654,0.00007081184,0.00039768865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801695,0.000012615754,0.00082786684,0.00013576461,0.00045894366,0.0005478344],"domain_scores_gemma":[0.9989616,0.00015134354,0.0002130922,0.00027939098,0.00012684669,0.00026771874],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068813027,0.00027067467,0.00034793172,0.00018022172,0.00022600588,0.00026013228,0.00028527607,0.00014346771,0.00033761474],"category_scores_gemma":[0.00014190699,0.00021590464,0.00011823825,0.00034998666,0.0000491503,0.00025335117,0.00006981069,0.0005638633,0.0008009752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011537371,0.00004344305,0.0006149996,0.00010986225,0.000159428,0.000017524944,0.0005007044,0.87653273,0.0006182332,0.008805739,0.0025214688,0.110064335],"study_design_scores_gemma":[0.0005990777,0.00004829286,0.00046493637,0.0001274814,0.000042911503,0.00022702287,0.00025411142,0.9857957,0.0017596948,0.0021764734,0.0080265105,0.0004778169],"about_ca_topic_score_codex":8.326854e-7,"about_ca_topic_score_gemma":4.7884936e-7,"teacher_disagreement_score":0.5667754,"about_ca_system_score_codex":0.00032365505,"about_ca_system_score_gemma":0.000025065076,"threshold_uncertainty_score":0.999977},"labels":[],"label_agreement":null},{"id":"W2146336822","doi":"10.1287/inte.1110.0583","title":"Universal Tool for Vaccine Scheduling: Applications for Children and Adults","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Oak Ridge Institute for Science and Education; Centers for Disease Control and Prevention; Georgia Institute of Technology; U.S. Department of Energy","keywords":"Schedule; Vaccination; Immunization; Scheduling (production processes); Computer science; Disease control; Medicine; Disease; Health care; Operations research; Environmental health; Operations management; Engineering; Immunology; Political science","score_opus":0.021082069023598916,"score_gpt":0.2738359357310502,"score_spread":0.25275386670745126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146336822","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5821581,0.00023422096,0.3702699,0.002148636,0.00032324035,0.005282921,0.0001441225,0.00019089752,0.039247975],"genre_scores_gemma":[0.985859,0.0003779256,0.011899425,0.00054974726,0.0007643523,0.00004951997,0.000018017245,0.000018202734,0.00046385633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909186,0.0000044549943,0.00030373814,0.00013786687,0.00016748006,0.00029462323],"domain_scores_gemma":[0.9992001,0.00009563932,0.00021809863,0.00012091102,0.00019552486,0.0001697129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039502163,0.000120540404,0.00018141433,0.00013842019,0.0006854302,0.00010114627,0.00022141173,0.000102319114,0.000048329322],"category_scores_gemma":[0.00006388978,0.00010129895,0.00010508355,0.00016455894,0.00001637009,0.00022646454,0.000019831425,0.0001583126,0.000011317336],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010775675,0.00020190641,0.011159786,0.0000410513,0.0002555256,0.0000017323338,0.0065593245,0.00012132194,0.000020662677,0.8693875,0.00407965,0.10709399],"study_design_scores_gemma":[0.033186253,0.0030144656,0.15089625,0.00028199964,0.0013958998,0.00008235914,0.021253953,0.0031530538,0.0012093346,0.30654344,0.4758865,0.0030965093],"about_ca_topic_score_codex":0.000015381731,"about_ca_topic_score_gemma":0.00005350905,"teacher_disagreement_score":0.56284404,"about_ca_system_score_codex":0.00007137381,"about_ca_system_score_gemma":0.00014887961,"threshold_uncertainty_score":0.5271844},"labels":[],"label_agreement":null},{"id":"W2147714773","doi":"10.1287/inte.1100.0520","title":"Taking the Politics Out of Paving: Achieving Transportation Asset Management Excellence Through OR","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of New Brunswick; Transport Canada","funders":"U.S. Department of Transportation","keywords":"Asset management; Asset (computer security); Business; Heuristic; Operations research; Transport engineering; Finance; Computer science; Engineering; Computer security","score_opus":0.20771048777770518,"score_gpt":0.372130339538934,"score_spread":0.1644198517612288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147714773","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.281716,0.000033899913,0.1115822,0.0005283564,0.001613001,0.00063466595,0.000019381276,0.000059839887,0.6038127],"genre_scores_gemma":[0.9928634,0.0002601577,0.004089451,0.00047316818,0.00011051537,0.0000046198375,0.0000020973985,0.000010133761,0.0021864565],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968451,0.00002296343,0.0012436206,0.00019128833,0.0013910683,0.000305914],"domain_scores_gemma":[0.9976721,0.0002332993,0.0013541679,0.0004607033,0.00020635364,0.0000734193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018009598,0.0001838216,0.00027008372,0.0003546277,0.0003952899,0.00022959168,0.0009188883,0.000059049147,0.0011469863],"category_scores_gemma":[0.00010214383,0.0000954091,0.00015835061,0.0007502135,0.00015434554,0.0005428228,0.000056906905,0.000354668,0.00009255611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005321888,0.00018060887,0.026876261,0.0001018873,0.00040745098,0.0000560407,0.018433299,0.003040665,0.00005481338,0.5484824,0.0035026644,0.39833167],"study_design_scores_gemma":[0.0047664526,0.0010902815,0.260568,0.0006600733,0.00091120385,0.00011251611,0.0926161,0.0110232895,0.0039062088,0.21084405,0.41165394,0.0018478773],"about_ca_topic_score_codex":0.00000920148,"about_ca_topic_score_gemma":0.000025453255,"teacher_disagreement_score":0.7111474,"about_ca_system_score_codex":0.000047217094,"about_ca_system_score_gemma":0.00005952543,"threshold_uncertainty_score":0.9997661},"labels":[],"label_agreement":null},{"id":"W2149852331","doi":"10.1287/inte.1040.0113","title":"Bombardier Flexjet Significantly Improves Its Fractional Aircraft Ownership Operations","year":2005,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Crew; Charter; Engineering; Aeronautics; Operations research; Service (business); Operations management; Business; Marketing","score_opus":0.022731258156740753,"score_gpt":0.2711719378261229,"score_spread":0.24844067966938216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149852331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04079449,0.00014592527,0.8618012,0.0016965823,0.0010371992,0.00049617165,0.000051229406,0.00082737074,0.093149826],"genre_scores_gemma":[0.918729,0.0001547726,0.077395685,0.0011205006,0.0011348776,0.00001270764,0.000025932588,0.00006991825,0.0013565762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826175,0.000017209575,0.0006814363,0.00014683997,0.00048868585,0.00040406297],"domain_scores_gemma":[0.9991062,0.00010556993,0.00010507159,0.00021855027,0.00018725973,0.0002773107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062204036,0.0002625968,0.00026810408,0.0003218505,0.00028292663,0.000304576,0.00024645118,0.00016971354,0.00035995423],"category_scores_gemma":[0.00010042246,0.00021831773,0.00012246026,0.0003647909,0.00003389715,0.0004873533,0.00002306616,0.0008679708,0.00049014686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015493535,0.000028909151,0.00001845404,0.000010392169,0.0001112308,0.0000029681555,0.00014925502,0.97427636,0.0028699893,0.004408533,0.0021308877,0.015977552],"study_design_scores_gemma":[0.0006931632,0.00004937277,0.00029595994,0.00001963783,0.000057960835,0.00006238957,0.00019155793,0.9295841,0.0062943096,0.00020552638,0.06213868,0.0004073475],"about_ca_topic_score_codex":5.1404675e-7,"about_ca_topic_score_gemma":0.0000033120264,"teacher_disagreement_score":0.8779345,"about_ca_system_score_codex":0.00028667413,"about_ca_system_score_gemma":0.00010570935,"threshold_uncertainty_score":0.89027387},"labels":[],"label_agreement":null},{"id":"W2150863986","doi":"10.1287/inte.31.3.3.9636","title":"Value Analysis and Optimization of Reusable Containers at Canada Post","year":2001,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Supply chain; Container (type theory); Stock (firearms); Business; Operations research; Productivity; Stock control; Operations management; Inventory control; Control (management); Environmental economics; Computer science; Industrial organization; Marketing; Economics; Engineering","score_opus":0.00953963887279685,"score_gpt":0.19714735458446916,"score_spread":0.1876077157116723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150863986","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6100489,0.00008096137,0.024158485,0.0034852827,0.0006140327,0.00056826585,0.000014116908,0.000077028235,0.3609529],"genre_scores_gemma":[0.99210316,0.00010922393,0.0003980854,0.0060597844,0.00024412175,0.0000023016275,0.00005638203,0.000016011376,0.0010109503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862415,0.0000030897106,0.00050422014,0.00013747033,0.00047945583,0.0002516214],"domain_scores_gemma":[0.9990251,0.000028528582,0.00051333464,0.00019209777,0.00019790504,0.000043049804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035351855,0.00017080003,0.00031033083,0.0006425011,0.0001998903,0.00017099177,0.00018965507,0.00004698516,0.00050693756],"category_scores_gemma":[0.00004834016,0.00013738405,0.00009919526,0.0010009741,0.000043711312,0.00035730933,0.00011092143,0.00013617192,0.0000109651355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021030784,0.000052181385,0.02717509,0.00006373326,0.00081060326,0.000036478505,0.000034379256,0.92979157,0.000050592527,0.03275399,0.0072502154,0.0017708652],"study_design_scores_gemma":[0.002705558,0.00007847954,0.012036069,0.000061478386,0.0019961973,0.000021592356,0.0022705118,0.6932557,0.00013320147,0.0010068115,0.285669,0.0007653859],"about_ca_topic_score_codex":0.02099507,"about_ca_topic_score_gemma":0.029383441,"teacher_disagreement_score":0.38205424,"about_ca_system_score_codex":0.0002221343,"about_ca_system_score_gemma":0.00006070563,"threshold_uncertainty_score":0.9883278},"labels":[],"label_agreement":null},{"id":"W2151059370","doi":"10.1287/inte.30.6.17.11631","title":"A Decision Support System for Planning Remanufacturing at Nortel Networks","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Government of Ontario","keywords":"Remanufacturing; Plan (archaeology); Decision support system; Product (mathematics); Reverse logistics; Process (computing); Process management; Operations research; Computer science; Engineering; Business; Manufacturing engineering; Supply chain; Marketing","score_opus":0.013466556618708103,"score_gpt":0.23014641328825983,"score_spread":0.21667985666955172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151059370","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38209,0.00008277844,0.18535186,0.0006531764,0.0022121659,0.0026634496,0.000008032229,0.0007436361,0.42619488],"genre_scores_gemma":[0.9902909,0.000020929545,0.0010720132,0.002870113,0.0025663225,0.000036646452,0.000051251696,0.00007688221,0.0030149557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99729556,0.0000027609428,0.0009008801,0.0003084236,0.00068322924,0.00080912723],"domain_scores_gemma":[0.9986921,0.00013035907,0.00055936293,0.00038250486,0.00016464347,0.00007105164],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010801398,0.00037661535,0.00043334128,0.0006545974,0.0008090435,0.0009203339,0.0005138694,0.00014427662,0.0006110145],"category_scores_gemma":[0.00004121512,0.00030329978,0.00024960935,0.00045620315,0.00003491808,0.0007341731,0.00017192282,0.0003918529,0.00046428075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010767977,0.000051935087,0.00086371787,0.00034004333,0.00017480023,0.000216531,0.000076245626,0.8494655,0.0000031246911,0.008774397,0.027612107,0.11134481],"study_design_scores_gemma":[0.0031213046,0.0000697001,0.0011381364,0.0003008901,0.00025420694,0.000075059645,0.0018577881,0.2601076,0.00004089206,0.0024628222,0.72983617,0.0007353935],"about_ca_topic_score_codex":0.0000083489385,"about_ca_topic_score_gemma":0.0000062039508,"teacher_disagreement_score":0.7022241,"about_ca_system_score_codex":0.00062546215,"about_ca_system_score_gemma":0.00003233476,"threshold_uncertainty_score":0.9999419},"labels":[],"label_agreement":null},{"id":"W2152294486","doi":"10.1287/inte.1110.0590","title":"Kimberly-Clark Latin America Builds an Optimization-Based System for Machine Scheduling","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kimberly-Clark (Canada)","funders":"","keywords":"Scheduling (production processes); Sizing; Operations research; Production planning; Single-machine scheduling; Computer science; Job shop scheduling; Mathematical optimization; Production (economics); Engineering; Operations management; Schedule; Economics; Mathematics; Microeconomics","score_opus":0.021953395711161623,"score_gpt":0.22904342655600082,"score_spread":0.2070900308448392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152294486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014718863,0.000040588737,0.98648703,0.000021539705,0.00039336475,0.00021183309,0.000023951225,0.00040822334,0.010941591],"genre_scores_gemma":[0.38558626,0.000026347527,0.6137227,0.0002553082,0.00022129033,0.000018075767,0.0000663797,0.00007226076,0.000031405696],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998459,0.000009120633,0.0006803599,0.00016293034,0.00031216102,0.0003764516],"domain_scores_gemma":[0.9988907,0.000073003655,0.00023937553,0.00028239464,0.00019761431,0.0003168877],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031330768,0.000289678,0.00033632273,0.00035944264,0.00022665937,0.00015860217,0.00028817527,0.00016251166,0.00011484254],"category_scores_gemma":[0.000041421365,0.00024907733,0.00013873747,0.00042350232,0.000036447025,0.00019492058,0.00001091109,0.00042476898,0.00003259873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006543864,0.000042370593,0.000050856,0.00005256125,0.00008229453,0.0000041492717,0.00019156736,0.9925255,0.000012592193,0.0010629585,0.000032839052,0.005876851],"study_design_scores_gemma":[0.001107797,0.00014386949,0.000019350844,0.000059522714,0.00006845445,0.0000131674515,0.00039641192,0.9967645,0.0006722291,0.00006127881,0.0003624246,0.00033099725],"about_ca_topic_score_codex":0.0000033349538,"about_ca_topic_score_gemma":0.0000010297076,"teacher_disagreement_score":0.38411438,"about_ca_system_score_codex":0.0001700725,"about_ca_system_score_gemma":0.00007351601,"threshold_uncertainty_score":0.9999961},"labels":[],"label_agreement":null},{"id":"W2152306358","doi":"10.1287/inte.30.6.32.11625","title":"The Québec Ministry of Natural Resources Uses Linear Programming to Understand the Wood-Fiber Market","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts (Québec); Université Laval","funders":"","keywords":"Christian ministry; Government (linguistics); Negotiation; Linear programming; Natural resource; Industrial organization; Fiber; Yield (engineering); Business; Computer science; Environmental economics; Economics; Political science","score_opus":0.010329851943537449,"score_gpt":0.23616173908635188,"score_spread":0.22583188714281444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152306358","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72580343,0.0000337172,0.000011351173,0.0032330365,0.000053133215,0.00023644259,0.0000021704736,0.00001646386,0.27061024],"genre_scores_gemma":[0.92248034,0.00008205857,0.00029905204,0.0012888584,0.00015610305,0.0000032354837,7.9472187e-7,0.000014133319,0.075675406],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99872553,0.000013122804,0.000352629,0.000096849995,0.000498946,0.00031289665],"domain_scores_gemma":[0.99932015,0.00013896322,0.00016556753,0.00025741177,0.000008579414,0.00010934661],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047169052,0.00014258095,0.00012391865,0.000041065083,0.00046027362,0.00018534859,0.0005329513,0.000039742896,0.002906339],"category_scores_gemma":[0.00002703875,0.000067981375,0.0000953887,0.00032402275,0.0003025226,0.00009153374,0.000093486306,0.0003086457,0.0006709604],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013279788,0.00016860676,0.005170372,0.000031156633,0.00036731426,0.000021628963,0.008225366,0.035966147,0.000039243834,0.0029603972,0.26299167,0.68273014],"study_design_scores_gemma":[0.00022579519,0.000089093715,0.0026587031,0.000015311118,0.000031940344,0.000011875853,0.00085571327,0.0012987073,0.000030798972,0.00020951472,0.9944461,0.00012647104],"about_ca_topic_score_codex":0.00022052137,"about_ca_topic_score_gemma":0.00053730834,"teacher_disagreement_score":0.73145443,"about_ca_system_score_codex":0.00012304998,"about_ca_system_score_gemma":0.000025066494,"threshold_uncertainty_score":0.99800515},"labels":[],"label_agreement":null},{"id":"W2155355405","doi":"10.1287/inte.1050.0175","title":"Developing the Reflective Practitioner—Designing an Undergraduate Class","year":2006,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Reflective Practices in Education","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University; University of Strathclyde","keywords":"Reflection (computer programming); Class (philosophy); Reflective practice; Process (computing); Action (physics); Action research; Statement (logic); Mathematics education; Psychology; Medical education; Engineering ethics; Engineering; Computer science; Pedagogy; Medicine; Political science; Artificial intelligence","score_opus":0.0507119137437996,"score_gpt":0.3914413770110575,"score_spread":0.34072946326725795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155355405","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01437085,0.000021696389,0.07383211,0.017921107,0.00072083104,0.00033084498,0.0000010972058,0.00008548184,0.892716],"genre_scores_gemma":[0.9831021,0.00018150905,0.012187312,0.0018530157,0.0014702717,0.00001561739,0.0000062990594,0.000020718715,0.0011631795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978954,0.00019646002,0.00045794394,0.000176758,0.0008482872,0.00042515964],"domain_scores_gemma":[0.9978673,0.00058032665,0.00081458455,0.00020782197,0.00040511152,0.00012489606],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0025383786,0.0001662329,0.00015901006,0.00022213292,0.0023554184,0.0008733353,0.0004127452,0.000121231795,0.000033748438],"category_scores_gemma":[0.00045574454,0.0001153971,0.00007300358,0.0008115346,0.0002399204,0.0013225677,0.000026240416,0.00084718026,0.00010067995],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058868864,0.00008553119,0.00035347193,0.0000026826258,0.00006238599,0.0000044100284,0.003843002,0.0064619477,0.00021948243,0.9739867,0.0016474322,0.01327411],"study_design_scores_gemma":[0.00038739754,0.00011340175,0.0022063572,0.00003331457,0.000081868115,0.000038765822,0.020795764,0.0005786257,0.0010269698,0.58328146,0.39108413,0.00037193712],"about_ca_topic_score_codex":0.00019655394,"about_ca_topic_score_gemma":0.0006377578,"teacher_disagreement_score":0.9687312,"about_ca_system_score_codex":0.0013787458,"about_ca_system_score_gemma":0.0010707282,"threshold_uncertainty_score":0.9989434},"labels":[],"label_agreement":null},{"id":"W2162064622","doi":"10.1287/inte.2013.0702","title":"Editorial: The 10th Rothkopf Rankings of Universities’ Contributions to the INFORMS Practice Literature","year":2013,"lang":"en","type":"editorial","venue":"INFORMS Journal on Applied Analytics","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Visibility; Ranking (information retrieval); Yield (engineering); Norwegian; Library science; Sociology; Management; Geography; Computer science; Economics","score_opus":0.012210010728166132,"score_gpt":0.28122365836235536,"score_spread":0.2690136476341892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162064622","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002893863,0.00011677149,0.002252415,0.0047738515,0.96951604,0.00073736586,0.0005716833,0.00005443693,0.0219485],"genre_scores_gemma":[0.0009917802,0.00057316886,0.000061829545,0.0032167044,0.99290055,0.000019951642,0.0005363718,0.00004688644,0.0016527346],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99560046,0.00001311276,0.0010710016,0.00026993908,0.0024611684,0.0005843307],"domain_scores_gemma":[0.99036527,0.0010566618,0.0022976666,0.0008445098,0.0053741797,0.00006170224],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0020517632,0.000542492,0.00063084345,0.00062732113,0.0007471798,0.0024937992,0.00231075,0.00084429036,0.0001464422],"category_scores_gemma":[0.002333372,0.00027710458,0.0003019029,0.0018017839,0.00021581067,0.002720617,0.000556972,0.0033066813,0.000734023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031345416,0.00004779858,8.310172e-7,0.000111296686,0.00024284821,0.0000048519264,0.00024744665,0.0009335809,0.0000022790366,0.0223698,0.97333395,0.002391884],"study_design_scores_gemma":[0.00052198215,0.00003728946,0.000003214274,0.0004921193,0.00043863192,0.000007440136,0.001843407,0.00020867199,0.0000065153563,0.0013806868,0.9946714,0.00038866603],"about_ca_topic_score_codex":0.00022183948,"about_ca_topic_score_gemma":0.000029594332,"teacher_disagreement_score":0.023384534,"about_ca_system_score_codex":0.00031343938,"about_ca_system_score_gemma":0.00069534074,"threshold_uncertainty_score":0.9999681},"labels":[],"label_agreement":null},{"id":"W2162564886","doi":"10.1287/inte.1030.0055","title":"The Canadian Pacific Railway Transforms Operations by Using Models to Develop Its Operating Plans","year":2004,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Tonnage; Train; Productivity; Service (business); Operations research; Block (permutation group theory); Engineering; Suite; Fuel efficiency; Transport engineering; Rail freight transport; Heuristic; Operations management; Computer science; Business; Economics; Automotive engineering; Marketing","score_opus":0.039494680634362164,"score_gpt":0.2849870721228541,"score_spread":0.24549239148849195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162564886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26424804,0.000073186995,0.5283243,0.017051341,0.0007569519,0.0014283643,0.00026913168,0.00017814936,0.18767054],"genre_scores_gemma":[0.99384874,0.00007641411,0.004550332,0.000706947,0.00011410253,0.00000654646,0.000037265876,0.000014630029,0.00064501644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99847114,0.000013580667,0.00045149797,0.00011328415,0.00050617685,0.00044429756],"domain_scores_gemma":[0.99896467,0.000032406337,0.00007489842,0.000084916486,0.0003522904,0.0004907965],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0007609387,0.00013611808,0.0001259471,0.00017618101,0.0046398416,0.0008220084,0.0002390711,0.00009772984,0.000018700159],"category_scores_gemma":[0.00005578512,0.00009536989,0.000039545368,0.0007576379,0.00006226078,0.00043673345,0.0000029650632,0.00033841262,0.000030067426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006948948,0.00000729201,0.00002084681,0.0000010250934,0.000018445373,0.0000026253974,0.01421918,0.88326484,0.00006897207,0.101212084,0.00018194187,0.0009957864],"study_design_scores_gemma":[0.006247318,0.0004049877,0.00045440672,0.000668607,0.000284705,0.00007002702,0.18409663,0.3554506,0.004035921,0.0122663835,0.43241325,0.0036071506],"about_ca_topic_score_codex":0.008159806,"about_ca_topic_score_gemma":0.29292494,"teacher_disagreement_score":0.7296007,"about_ca_system_score_codex":0.0005928781,"about_ca_system_score_gemma":0.0022567527,"threshold_uncertainty_score":0.9984449},"labels":[],"label_agreement":null},{"id":"W2163689154","doi":"10.1287/inte.33.2.12.14465","title":"Applying Operations Research Techniques to Financial Markets","year":2003,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of Nottingham; London School of Economics and Political Science","keywords":"Financial market; Equity (law); Debt; Finance; Business; Financial modeling; Market data; Financial engineering; Economics","score_opus":0.13052742231364783,"score_gpt":0.42996205898317885,"score_spread":0.299434636669531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163689154","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039749987,0.00003124759,0.37397078,0.0013524868,0.0007705894,0.0015486602,0.000013788279,0.00009578395,0.58246666],"genre_scores_gemma":[0.9406807,0.0005100705,0.048872266,0.0032294,0.0005661817,0.00013678352,0.0000047626354,0.00003004401,0.005969809],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958791,0.000098638724,0.0009934658,0.0002580825,0.0023111955,0.00045948627],"domain_scores_gemma":[0.9976558,0.00037820838,0.00013976515,0.00046874408,0.00098588,0.00037159614],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.009318704,0.00016121937,0.00026889282,0.0014578891,0.0008836121,0.001159137,0.000633738,0.00014128086,0.00034391656],"category_scores_gemma":[0.0039564143,0.00010939024,0.00010830495,0.0024772221,0.00006715057,0.0003638247,0.000068332556,0.0007320645,0.00073215424],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017746007,0.00014306282,0.00079852226,0.0000021680191,0.000024716843,0.00003603579,0.0006829465,0.12590192,0.00030083518,0.28655174,0.12328118,0.4620994],"study_design_scores_gemma":[0.00024317145,0.00014121873,0.0004853263,0.000018592627,0.00000725559,0.000053420506,0.0005367234,0.003449237,0.0025565424,0.032218844,0.960068,0.00022169262],"about_ca_topic_score_codex":0.0000031720092,"about_ca_topic_score_gemma":0.000015421208,"teacher_disagreement_score":0.9009307,"about_ca_system_score_codex":0.00014045446,"about_ca_system_score_gemma":0.0004415373,"threshold_uncertainty_score":0.99987775},"labels":[],"label_agreement":null},{"id":"W2167612934","doi":"10.1287/inte.1030.0049","title":"Preferred Scenarios in the Sport of Curling","year":2004,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Federated Co-operatives (Canada)","funders":"","keywords":"Championship; Curling; Shot (pellet); World championship; Point (geometry); Class (philosophy); Advertising; Team sport; Tipping point (physics); Marketing; Psychology; Computer science; Engineering; Mathematics; Artificial intelligence; Athletes; Business","score_opus":0.03536256942786613,"score_gpt":0.23215535338871035,"score_spread":0.1967927839608442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167612934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9162068,0.00018262526,0.0017012646,0.00057470135,0.00021286332,0.00019669256,0.000014247755,0.00000974518,0.08090107],"genre_scores_gemma":[0.99827975,0.00049931486,0.00021172408,0.0007464471,0.00012940812,0.0000028001637,0.000005381162,0.000012644927,0.0001125142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99833655,7.460605e-7,0.001119112,0.00013512379,0.00013086511,0.00027762796],"domain_scores_gemma":[0.99883056,0.000025816602,0.00074728037,0.00029029092,0.00003678888,0.000069288966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010640785,0.00014851478,0.00038731642,0.00040963278,0.00009334414,0.00009385837,0.00039620657,0.00008482678,0.000097489276],"category_scores_gemma":[0.00003242596,0.000107038504,0.00014674796,0.00046163166,0.000052900483,0.000160004,0.000022444498,0.0004879062,0.00011006762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057505928,0.0003136977,0.093705036,0.00002821105,0.00006518141,0.000031037438,0.0019098845,0.2351353,0.0000058947735,0.6674648,0.00017852538,0.0011049389],"study_design_scores_gemma":[0.011414268,0.0012924098,0.3251521,0.0005310463,0.000106947664,0.00034709004,0.0048266063,0.059393052,0.00069524394,0.36286414,0.23128994,0.0020871453],"about_ca_topic_score_codex":0.000041557167,"about_ca_topic_score_gemma":0.000022397473,"teacher_disagreement_score":0.30460063,"about_ca_system_score_codex":0.00011548516,"about_ca_system_score_gemma":0.00006074741,"threshold_uncertainty_score":0.43649036},"labels":[],"label_agreement":null},{"id":"W2169931118","doi":"10.1287/inte.1070.0332","title":"Chrysler and J. D. Power: Pioneering Scientific Price Customization in the Automobile Industry","year":2008,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Chrysler (Canada)","funders":"","keywords":"Lease; Economics; Product (mathematics); Market power; Incentive; Pricing strategies; Multinomial logistic regression; Industrial organization; Business; Microeconomics; Finance; Monopoly; Computer science","score_opus":0.020588390831361564,"score_gpt":0.23045557100791123,"score_spread":0.20986718017654965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169931118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9602237,0.00003056522,0.0002059074,0.0002578733,0.00023539772,0.00015832494,5.090283e-7,0.000032818763,0.038854957],"genre_scores_gemma":[0.9985185,0.000026494468,0.00004995018,0.0010044946,0.00019014798,0.0000049770483,0.00000444012,0.000012270149,0.00018870572],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99885225,0.000004344745,0.00035874418,0.00013380418,0.00039879396,0.00025203134],"domain_scores_gemma":[0.9994618,0.000049082457,0.0002177553,0.00016893576,0.00008073697,0.000021657832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009150113,0.00014386747,0.00014906355,0.0005430663,0.00053355045,0.0007067816,0.0002261488,0.000108662665,0.00012755481],"category_scores_gemma":[0.000051270155,0.00009741991,0.000043668533,0.0009301474,0.00008616298,0.0006785452,0.00007271094,0.0007420635,0.00005215773],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005023294,0.00091306295,0.7773356,0.0003350563,0.00017241841,0.0006606479,0.0072709955,0.024399634,0.0024755104,0.03358191,0.06231161,0.09004125],"study_design_scores_gemma":[0.0028865158,0.000036445388,0.50294536,0.00019071343,0.00015581035,0.00044335672,0.0029324135,0.02814634,0.00007328213,0.00080648175,0.4604604,0.00092290674],"about_ca_topic_score_codex":0.000010106317,"about_ca_topic_score_gemma":0.0000066856705,"teacher_disagreement_score":0.39814878,"about_ca_system_score_codex":0.00004067715,"about_ca_system_score_gemma":0.000044973283,"threshold_uncertainty_score":0.6815511},"labels":[],"label_agreement":null},{"id":"W2203748571","doi":"10.1287/inte.2015.0815","title":"ASP, The Art and Science of Practice: Academia-Industry Interfacing in Operations Research in Montréal","year":2015,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Business Strategy and Innovation","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"HEC Montréal","funders":"","keywords":"Interfacing; Engineering ethics; Field (mathematics); Management; Technology transfer; Spin offs; Engineering; Engineering management; Sociology; Political science; Business; Knowledge management; Computer science; Mathematics; Economics; Industrial organization; Law","score_opus":0.09572344529637218,"score_gpt":0.3560162520420642,"score_spread":0.26029280674569205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203748571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90868294,0.00003466619,0.00014167243,0.009603127,0.000104815466,0.00016564186,3.0995366e-7,0.000006715977,0.08126009],"genre_scores_gemma":[0.9987055,0.000024345698,0.00013177657,0.00089183875,0.00019322096,0.0000036299143,0.0000011559805,0.0000052971977,0.000043218293],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985826,0.00000761652,0.0004721971,0.00010236118,0.00059948064,0.00023572662],"domain_scores_gemma":[0.9990228,0.00006114794,0.00016382814,0.00011412118,0.00061838015,0.00001975414],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00525909,0.00008288342,0.00012413865,0.00096171396,0.00020456311,0.00045840672,0.00024146415,0.00016284353,0.000007587214],"category_scores_gemma":[0.0006600455,0.000053852833,0.000011003626,0.0026660932,0.00023715729,0.0018176022,0.00016070623,0.0023204612,0.000026915983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033854955,0.00018743296,0.006759559,0.000057523805,0.000020594116,0.000024822775,0.0009218401,0.18257028,0.0013853721,0.7827056,0.0020998516,0.022928592],"study_design_scores_gemma":[0.009480784,0.00028302433,0.09051797,0.001572265,0.00010682031,0.0002446931,0.1009286,0.5843432,0.0037058021,0.123325795,0.08419256,0.0012984886],"about_ca_topic_score_codex":0.00021538,"about_ca_topic_score_gemma":0.00012200015,"teacher_disagreement_score":0.6593798,"about_ca_system_score_codex":0.00011831036,"about_ca_system_score_gemma":0.00025748712,"threshold_uncertainty_score":0.9999812},"labels":[],"label_agreement":null},{"id":"W2537516281","doi":"10.1287/inte.2016.0863","title":"Power System Operator in Mexico Reveals Millions in Savings by Updating Its Short-Term Thermal Unit Commitment Model","year":2016,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Electric Power System Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Power system simulation; Integer programming; Operations research; Operator (biology); Lagrangian relaxation; Computer science; Thermal power station; Operational planning; Process (computing); Mathematical optimization; Term (time); Economic dispatch; Electric power system; Engineering; Power (physics); Economics; Mathematics; Operating system; Waste management","score_opus":0.012622626418105801,"score_gpt":0.22828881889855795,"score_spread":0.21566619248045216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537516281","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8955849,0.00018997658,0.067002796,0.00016205333,0.0002677495,0.0007220382,0.00005039578,0.0002003217,0.035819806],"genre_scores_gemma":[0.99905026,0.00010214411,0.00048810855,0.000110142944,0.000034013407,0.000025832605,0.00000806553,0.000055255114,0.00012617992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978135,0.00002271928,0.0010813575,0.0001585726,0.00041275876,0.000511096],"domain_scores_gemma":[0.9992582,0.00007204805,0.00015649686,0.00024983042,0.000080233156,0.00018315195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006973302,0.0002896843,0.0004125063,0.00052118173,0.00007906221,0.000119569515,0.00032545766,0.00017310263,0.000026493233],"category_scores_gemma":[0.000026179347,0.00020359413,0.0000659011,0.0005529442,0.00001442649,0.00035959622,0.00003769683,0.0004564526,0.000053498454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003656806,0.000067109875,0.0066380063,0.000080157144,0.000101423764,0.00003410312,0.0005890986,0.96768224,0.016623907,0.0055979528,0.0011463622,0.0014030915],"study_design_scores_gemma":[0.0025898574,0.000121882,0.0014815719,0.0015823453,0.00003812685,0.000084783955,0.00050305686,0.9838378,0.008382195,0.00007387423,0.00040947535,0.0008950512],"about_ca_topic_score_codex":9.563408e-7,"about_ca_topic_score_gemma":0.0000059087843,"teacher_disagreement_score":0.10346539,"about_ca_system_score_codex":0.0010145965,"about_ca_system_score_gemma":0.00006402686,"threshold_uncertainty_score":0.8302328},"labels":[],"label_agreement":null},{"id":"W2594829375","doi":"10.1287/inte.2016.0864","title":"A Review of Scheduling Problems and Research Opportunities in Motion Picture Exhibition","year":2017,"lang":"en","type":"review","venue":"INFORMS Journal on Applied Analytics","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Exhibition; Movie theater; Film industry; Scheduling (production processes); Computer science; Context (archaeology); Scale (ratio); Engineering; Data science; Multimedia; Visual arts; Art; Operations management; History; Geography","score_opus":0.4224786817422227,"score_gpt":0.3940462467131995,"score_spread":0.028432435029023206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594829375","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011467437,0.97721064,0.000070057285,0.00018133836,0.0001340995,0.000543346,0.000047084388,0.000004688241,0.021797288],"genre_scores_gemma":[0.0001406875,0.99903965,0.000113006296,0.0001185161,0.0001989466,0.000039949042,0.00003640317,0.000023886088,0.00028892947],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975369,0.000017752647,0.0017743795,0.00022124933,0.00014193596,0.00030776183],"domain_scores_gemma":[0.9973281,0.00012239118,0.0019731594,0.00032549747,0.0001343732,0.00011648378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034060872,0.0002621827,0.0020406372,0.0012399455,0.00018263886,0.000117750875,0.00026449983,0.00023721671,0.00003895184],"category_scores_gemma":[0.00048181965,0.0002068309,0.00025852775,0.0002731366,0.00014491,0.00016156459,0.00010469893,0.0012144153,0.00003317766],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053986055,0.000052000374,0.000024027577,0.10776359,0.00015168257,0.000012864947,0.0002558985,0.000012725438,2.4618634e-8,0.03922742,0.00075838226,0.851736],"study_design_scores_gemma":[0.00022750736,0.000060305858,0.000008309611,0.09452972,0.00006121066,0.000036596557,0.000113295115,0.000024878516,1.1085379e-7,0.0064021014,0.8983465,0.00018948018],"about_ca_topic_score_codex":0.0000060420116,"about_ca_topic_score_gemma":0.000006165733,"teacher_disagreement_score":0.8975881,"about_ca_system_score_codex":0.00019159472,"about_ca_system_score_gemma":0.0001259912,"threshold_uncertainty_score":0.84343195},"labels":[],"label_agreement":null},{"id":"W2762403164","doi":"10.1287/inte.2017.0906","title":"Calibrated Route Finder: Improving the Safety, Environmental Consciousness, and Cost Effectiveness of Truck Routing in Sweden","year":2017,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Truck; Standardization; Fuel efficiency; Process (computing); Operations research; Routing (electronic design automation); Transport engineering; Analytics; Externality; Key (lock); Environmental economics; Telematics; Computer science; Business; Engineering; Computer security; Economics; Automotive engineering; Telecommunications; Microeconomics","score_opus":0.012539391289504149,"score_gpt":0.22985461102650862,"score_spread":0.21731521973700446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762403164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9691645,0.000050302493,0.011099769,0.00008375324,0.0002738384,0.00064336846,0.000023821009,0.000047454476,0.018613216],"genre_scores_gemma":[0.99966884,0.0001612712,0.000045834677,0.00004037528,0.00002245196,0.0000044530775,0.000006881266,0.00001593856,0.00003395584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99916893,0.000012167739,0.00036040024,0.000083037594,0.00018649473,0.00018897737],"domain_scores_gemma":[0.9994423,0.00006786746,0.00020180609,0.00021702186,0.000009098111,0.000061911145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005152446,0.00014033148,0.00020323739,0.00010388268,0.00023297349,0.0001899592,0.00023403332,0.00006402702,0.000013671946],"category_scores_gemma":[0.000024455583,0.00009721937,0.000039601928,0.000062961284,0.00011938043,0.00017635446,0.00009531469,0.00022791511,0.000004533064],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007961652,0.00018573491,0.3438426,0.0007948126,0.00078208535,0.00010220359,0.001453341,0.35759687,0.011695942,0.09632095,0.00022208315,0.1862072],"study_design_scores_gemma":[0.0050289487,0.00009485606,0.61484903,0.00028490846,0.000094241565,0.00004283728,0.001105668,0.36665624,0.007489331,0.0011386231,0.002700974,0.00051435916],"about_ca_topic_score_codex":0.000014044831,"about_ca_topic_score_gemma":0.000048743754,"teacher_disagreement_score":0.27100644,"about_ca_system_score_codex":0.0001328896,"about_ca_system_score_gemma":0.000017069278,"threshold_uncertainty_score":0.39644906},"labels":[],"label_agreement":null},{"id":"W2764060445","doi":"10.1287/inte.2017.0918","title":"Introduction: 2016 Daniel H. Wagner Prize for Excellence in Operations Research Practice","year":2017,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Staffing; Competition (biology); Analytics; CLARITY; Excellence; Productivity; Originality; Automotive industry; Quality (philosophy); Presentation (obstetrics); Resource (disambiguation); Engineering management; Operations research; Computer science; Management; Operations management; Engineering; Creativity; Data science; Political science; Economics","score_opus":0.05058330178861114,"score_gpt":0.33731420827402964,"score_spread":0.2867309064854185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764060445","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012254262,0.00021662959,0.23597112,0.037404798,0.0040142178,0.002563316,0.000024210292,0.00028559836,0.70726585],"genre_scores_gemma":[0.9684016,0.0019980033,0.010928248,0.00052122603,0.0019141608,0.00010314773,0.00002048356,0.000056015637,0.016057104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988115,0.000008802535,0.00038154426,0.00012693384,0.0003671042,0.0003041327],"domain_scores_gemma":[0.99909604,0.00006507371,0.00006785048,0.00042834552,0.00023798006,0.00010473509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011578542,0.00011893495,0.00013529493,0.00032638563,0.0005316274,0.00057991943,0.0003728929,0.00007516425,0.000074543634],"category_scores_gemma":[0.00041451066,0.00009674728,0.000043407934,0.00016735034,0.00007524771,0.00057701045,0.000060293947,0.000403883,0.000120630946],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016825221,0.00016661562,0.00006131767,0.00011034343,0.00013509435,0.000021465703,0.0005141094,0.45113856,0.00071448507,0.2797392,0.24462298,0.022607597],"study_design_scores_gemma":[0.0008624999,0.0000794323,0.00023985276,0.000034685567,0.000016686841,0.000015216831,0.00057582575,0.08089538,0.0005859827,0.001031277,0.9154951,0.00016804867],"about_ca_topic_score_codex":0.0000050013914,"about_ca_topic_score_gemma":0.000057768175,"teacher_disagreement_score":0.9561474,"about_ca_system_score_codex":0.00018676202,"about_ca_system_score_gemma":0.000057664063,"threshold_uncertainty_score":0.5592177},"labels":[],"label_agreement":null},{"id":"W2785506546","doi":"10.1287/inte.2017.0930","title":"Discrete-Event Simulation Modeling Unlocks Value for the Jansen Potash Project","year":2018,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Amec Foster Wheeler (Canada); BHP (Canada)","funders":"","keywords":"Net present value; Discrete event simulation; Production (economics); Engineering; Operations research; Dice; Potash; Operations management; Event (particle physics); Economics; Simulation; Mathematics","score_opus":0.03419948282742167,"score_gpt":0.2935306931228241,"score_spread":0.2593312102954024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785506546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027647488,0.000015383283,0.96345514,0.00007658676,0.00024253986,0.0003511218,0.000011217707,0.00012871422,0.008071831],"genre_scores_gemma":[0.99277025,0.00008764045,0.006193978,0.00019970805,0.0006309299,0.000014759531,0.0000063650436,0.00003573184,0.000060654773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906665,0.0000023844111,0.00044054107,0.00008694132,0.00013377248,0.00026973814],"domain_scores_gemma":[0.9994688,0.00009058737,0.0001041365,0.00021329179,0.00006211772,0.0000610713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047544777,0.00015984352,0.00016668133,0.000119096985,0.00023130799,0.00015508886,0.00022494467,0.000092424125,0.00001242625],"category_scores_gemma":[0.000022061518,0.00010535216,0.000120456345,0.00010176575,0.00002633418,0.0001068499,0.000026046377,0.000273231,0.000017665201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036579906,0.0000049497185,0.000006139758,0.000010537846,0.00006932959,3.031428e-7,0.00027368346,0.9860422,0.000016487971,0.00510992,0.0008779169,0.0075519234],"study_design_scores_gemma":[0.0002566078,0.00009695966,0.0000040365194,0.000024636394,0.000041969837,0.0000074152763,0.00015593455,0.97587985,0.00024499316,0.0033216022,0.019807953,0.00015806354],"about_ca_topic_score_codex":0.0000028660781,"about_ca_topic_score_gemma":0.000003702207,"teacher_disagreement_score":0.96512276,"about_ca_system_score_codex":0.00014672517,"about_ca_system_score_gemma":0.000035607434,"threshold_uncertainty_score":0.42961365},"labels":[],"label_agreement":null},{"id":"W2885299987","doi":"10.1287/inte.2018.0947","title":"Solving the Whistler-Blackcomb Mega Day Challenge","year":2018,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Whistler; Mega-; Advertising; Aeronautics; Engineering; Business; Physics","score_opus":0.023405799602491572,"score_gpt":0.281363314647048,"score_spread":0.25795751504455644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885299987","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13961007,0.00018886488,0.00243654,0.009295611,0.0015684777,0.00038140616,0.0000062637027,0.00009583378,0.84641695],"genre_scores_gemma":[0.9878597,0.0004943002,0.00033294185,0.0049821,0.0026803722,0.0000030746392,0.0000041065687,0.000026697504,0.0036166764],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983762,0.0000050351373,0.00055683637,0.00013493944,0.0005311023,0.00039592327],"domain_scores_gemma":[0.9987801,0.00004468743,0.0003031625,0.00045414112,0.00019330875,0.00022464668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000707506,0.00021048306,0.00031277927,0.00015124837,0.00042844363,0.00012994037,0.00028876346,0.00010116998,0.0008270406],"category_scores_gemma":[0.000035428566,0.000112034206,0.00016414009,0.00021629404,0.00023687762,0.0001407234,0.000068284964,0.0008037297,0.00031788045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031767637,0.0009179771,0.0079577295,0.0002898572,0.0019424423,0.0003898313,0.019743433,0.0004621253,0.00054231146,0.14717281,0.3273466,0.49005812],"study_design_scores_gemma":[0.0012788182,0.0012791979,0.003891167,0.00019310114,0.00014973979,0.00034227993,0.0008434453,0.0029537342,0.00090757635,0.0012967805,0.9865934,0.00027075893],"about_ca_topic_score_codex":0.0000021314852,"about_ca_topic_score_gemma":0.000007545645,"teacher_disagreement_score":0.8482497,"about_ca_system_score_codex":0.000115482486,"about_ca_system_score_gemma":0.00012554883,"threshold_uncertainty_score":0.90555143},"labels":[],"label_agreement":null},{"id":"W2902906171","doi":"10.1287/inte.2018.0959","title":"Metro Uses a Simulation-Optimization Approach to Improve Fare-Collection Shift Scheduling","year":2018,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Integer programming; Scheduling (production processes); Discrete event simulation; Computer science; Operations research; Mathematical optimization; Simulation; Engineering; Mathematics; Algorithm","score_opus":0.026314478494525704,"score_gpt":0.30246534036897915,"score_spread":0.2761508618744534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902906171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016818628,0.0000041176745,0.9310983,0.00024197064,0.00035576845,0.0002828231,0.000004898056,0.00011131751,0.051082194],"genre_scores_gemma":[0.9130614,0.000016629101,0.08522644,0.0006487643,0.0006581506,0.000008220616,0.00002630641,0.000017680086,0.0003364235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983385,0.000026718324,0.0004887357,0.00018881017,0.0006400191,0.0003171716],"domain_scores_gemma":[0.9987949,0.00010904875,0.00031885988,0.00012436091,0.00040056097,0.00025225224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074634945,0.00015387704,0.0001817439,0.0004962003,0.0012188788,0.00041644927,0.00018274746,0.00015345565,0.00008856366],"category_scores_gemma":[0.00025456893,0.00013743243,0.000076726305,0.0011851484,0.00008624846,0.00040332534,0.0000082536935,0.00025672925,0.000044809844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009440189,0.000048787675,0.0007383068,0.0000041372678,0.00003365667,3.8715453e-7,0.0064960574,0.9824785,0.000005308117,0.008519519,0.0000844541,0.0014964952],"study_design_scores_gemma":[0.00074963394,0.00021430416,0.0013660726,0.000031528267,0.000076528086,8.748121e-7,0.0043912046,0.9862216,0.00009700397,0.00075967703,0.0057397564,0.0003518186],"about_ca_topic_score_codex":0.000045826848,"about_ca_topic_score_gemma":0.000073559546,"teacher_disagreement_score":0.89624274,"about_ca_system_score_codex":0.00022858357,"about_ca_system_score_gemma":0.00022817297,"threshold_uncertainty_score":0.9374753},"labels":[],"label_agreement":null},{"id":"W2916399584","doi":"10.1287/inte.2018.0969","title":"Automated Pathologist Scheduling at The Ottawa Hospital","year":2019,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Electricity Association; Ottawa Hospital; University of Ottawa","funders":"Department of Pathology and Laboratory Medicine, University of North Carolina School of Medicine","keywords":"Scheduling (production processes); Medical laboratory; Medicine; Computer science; Medical physics; Medical emergency; Pathology; Operations management; Engineering","score_opus":0.030700366507547122,"score_gpt":0.3653847770548557,"score_spread":0.3346844105473086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916399584","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9270388,0.00004493748,0.0042631365,0.006991794,0.0015845959,0.0010383454,0.000017940545,0.00029045407,0.058729995],"genre_scores_gemma":[0.9866652,0.0001277276,0.004029028,0.004824002,0.0003654858,0.000024702893,0.00004735793,0.00003201915,0.0038844594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977727,0.00009568747,0.0009749896,0.00017840065,0.00044340742,0.00053482753],"domain_scores_gemma":[0.9981619,0.000309283,0.0005778904,0.0004012098,0.00034774025,0.00020198147],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017342693,0.00019469399,0.00028122056,0.00014560236,0.001899247,0.00009601401,0.00026748836,0.00027346876,0.0009945962],"category_scores_gemma":[0.00023308526,0.00011124671,0.000099274934,0.0003699386,0.0000613678,0.00015629613,0.00009077637,0.0014576735,0.0038945342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023158808,0.00023005554,0.0708376,0.00020066147,0.00021204309,0.000041628824,0.0069680866,0.8116127,0.00044930767,0.06878421,0.03255429,0.007877777],"study_design_scores_gemma":[0.0049780966,0.00094931066,0.027033655,0.0005427837,0.000114005576,0.000080465856,0.014719069,0.7851032,0.00014079048,0.0011632376,0.16404991,0.0011254628],"about_ca_topic_score_codex":0.000012857487,"about_ca_topic_score_gemma":0.00002422051,"teacher_disagreement_score":0.13149562,"about_ca_system_score_codex":0.00046692847,"about_ca_system_score_gemma":0.00047114684,"threshold_uncertainty_score":0.99991864},"labels":[],"label_agreement":null},{"id":"W2917137472","doi":"10.1287/inte.2018.0972","title":"Operations Research Enables Auction to Repurpose Television Spectrum for Next-Generation Wireless Technologies","year":2019,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Spectrum auction; Wireless; Telecommunications; Business; Computer science; Advertising; Marketing; Auction theory","score_opus":0.23834671815167754,"score_gpt":0.4288633834914778,"score_spread":0.19051666533980025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917137472","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54296464,0.000027402413,0.433759,0.014838093,0.0007803874,0.0021149532,0.000030793304,0.00020237797,0.005282377],"genre_scores_gemma":[0.99032354,0.000084865824,0.0025524374,0.00035317492,0.00042225595,0.00010741648,0.000016239166,0.000020528594,0.0061195157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964938,0.000055484437,0.0010553489,0.0004592806,0.0014884875,0.0004475578],"domain_scores_gemma":[0.99727106,0.00058629067,0.00024515597,0.000834231,0.00089047896,0.00017278918],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0049655107,0.00019348164,0.00031940299,0.0013584868,0.0012818894,0.001455751,0.00087513286,0.0001972086,0.00023822402],"category_scores_gemma":[0.001142053,0.0001333658,0.00015370373,0.0021019964,0.000119936165,0.00076060416,0.00013970911,0.000635697,0.0016858715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020951894,0.0001409849,0.000055034998,0.0000067755586,0.000037381797,0.000001204461,0.0003945531,0.23882498,0.031942476,0.5399103,0.015231295,0.17324546],"study_design_scores_gemma":[0.0011280566,0.0010738177,0.00011651655,0.000061372164,0.000031256637,0.00008639105,0.015337268,0.19831288,0.101787865,0.29845685,0.38300517,0.0006025428],"about_ca_topic_score_codex":0.0000031286659,"about_ca_topic_score_gemma":0.00001896813,"teacher_disagreement_score":0.44735894,"about_ca_system_score_codex":0.00026606215,"about_ca_system_score_gemma":0.00020048016,"threshold_uncertainty_score":0.99958086},"labels":[],"label_agreement":null},{"id":"W3000986316","doi":"10.1287/inte.2019.1022","title":"Analytics and Optimization Reduce Sewage Overflows to Protect Community Waterways in Kentucky","year":2020,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Urban Stormwater Management Solutions","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tetra Tech (Canada)","funders":"","keywords":"Combined sewer; Analytics; Metropolitan area; Sanitary sewer; Routing (electronic design automation); Maximization; Environmental science; General partnership; Computer science; Environmental engineering; Computer network; Stormwater; Business; Data science; Finance; Geography","score_opus":0.03268766417471507,"score_gpt":0.2363283965739841,"score_spread":0.20364073239926905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000986316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7638318,0.000008326492,0.16766323,0.005146515,0.00013108847,0.001527665,0.000021390553,0.00013549734,0.0615345],"genre_scores_gemma":[0.98883444,0.000035774814,0.008074791,0.0027242403,0.00005530566,0.00001146392,0.000009704446,0.00002168374,0.00023259717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984384,0.000040332186,0.0005187412,0.00018811684,0.00044963325,0.0003647616],"domain_scores_gemma":[0.9991415,0.000031291802,0.00017886281,0.00027176033,0.000014151,0.00036242526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058054755,0.00021232806,0.00024178352,0.00017347248,0.0003227086,0.00018003743,0.0003836079,0.00007672749,0.00027661692],"category_scores_gemma":[0.0000713023,0.00017734703,0.000052341267,0.0007360173,0.00008790106,0.00032779612,0.00040399423,0.00077318464,0.00019923231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009598075,0.000081477796,0.0029809794,0.00001527132,0.000028870403,0.000013123658,0.0020002217,0.98954755,0.0005254576,0.00025595457,0.002758347,0.0016967548],"study_design_scores_gemma":[0.0042161406,0.0015541551,0.02841586,0.00014613276,0.00022011636,0.000059930266,0.0032722526,0.8912203,0.0024774275,0.002084918,0.06467601,0.0016567822],"about_ca_topic_score_codex":0.000056867102,"about_ca_topic_score_gemma":0.00004940659,"teacher_disagreement_score":0.22500266,"about_ca_system_score_codex":0.0004007084,"about_ca_system_score_gemma":0.000014873306,"threshold_uncertainty_score":0.7232002},"labels":[],"label_agreement":null},{"id":"W3012135359","doi":"10.1287/inte.2020.1031","title":"A Decision Support System for Attended Home Services","year":2020,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Decision support system; Business; Electricity; Distribution (mathematics); Operations research; Operations management; Marketing; Computer science; Engineering","score_opus":0.014227646118997355,"score_gpt":0.22602564371901762,"score_spread":0.21179799760002027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012135359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00340229,0.000012886981,0.9902187,0.000062058214,0.00031953526,0.00015893874,0.000022459717,0.00026861692,0.0055345246],"genre_scores_gemma":[0.971018,0.000061443694,0.02813219,0.0003868135,0.00030620975,0.000006858889,0.000028274893,0.00003685251,0.000023338594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990177,0.0000013093697,0.0004127722,0.00010257557,0.00022801904,0.00023760869],"domain_scores_gemma":[0.99942786,0.00006171518,0.000118181546,0.00011671862,0.000065734406,0.00020980994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104965766,0.00017224684,0.00023540869,0.00010206164,0.0001075465,0.00011916539,0.00018471332,0.000086805616,0.000014472933],"category_scores_gemma":[0.000013034623,0.00013733828,0.00008473987,0.00012744956,0.000011226665,0.0001250173,0.000018436853,0.00024193224,0.000065571745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009186598,0.0000052907,0.000005750207,0.00020949235,0.000046757825,0.0000072594203,0.00013591588,0.9870995,0.000046743793,0.0039329543,0.00043959398,0.007978847],"study_design_scores_gemma":[0.0017314896,0.00022525855,0.000049122667,0.00008531308,0.00008168177,0.000043045213,0.0006285854,0.9673878,0.002551994,0.0032206515,0.02358329,0.00041180025],"about_ca_topic_score_codex":1.3756745e-7,"about_ca_topic_score_gemma":4.2574482e-7,"teacher_disagreement_score":0.9676157,"about_ca_system_score_codex":0.000108450826,"about_ca_system_score_gemma":0.000017289209,"threshold_uncertainty_score":0.5600493},"labels":[],"label_agreement":null},{"id":"W3044751814","doi":"10.1287/inte.2020.1027","title":"Barrick’s Turquoise Ridge Gold Mine Optimizes Underground Production Scheduling Operations","year":2020,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Barrick Gold (Canada)","funders":"","keywords":"Production (economics); Time horizon; Schedule; Resource (disambiguation); Horizon; Computer science; Operations research; Mining engineering; Engineering; Business; Mathematics; Finance; Economics","score_opus":0.026205937141039422,"score_gpt":0.2249408667407473,"score_spread":0.19873492959970787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044751814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77478105,0.0001220753,0.1900462,0.0024847055,0.0008935412,0.00043149668,0.000023646387,0.00087559887,0.030341696],"genre_scores_gemma":[0.95580155,0.0004718903,0.042074956,0.0006219344,0.000833637,0.000008241221,0.00001903142,0.000044821427,0.00012395775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989586,0.000002816119,0.00050665234,0.00013682562,0.00014439935,0.0002507203],"domain_scores_gemma":[0.9994472,0.000018859364,0.00007533645,0.0001719686,0.00004951997,0.00023710643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017497678,0.00020198444,0.00024278689,0.00013495104,0.00012294966,0.0002590695,0.00019903482,0.000084784304,0.0000698907],"category_scores_gemma":[0.000055458808,0.00017977062,0.00009154246,0.000214247,0.00002415224,0.00029567513,0.00003133551,0.00052951556,0.000104013976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020669648,0.000011603563,0.00001859449,0.00002310008,0.00007287059,0.000004047683,0.00022695029,0.9895909,0.0010175115,0.0036698156,0.0036577375,0.001686229],"study_design_scores_gemma":[0.000506514,0.00013212523,0.000035554873,0.000044700137,0.00006357499,0.000064844375,0.00045100958,0.97882056,0.005094517,0.0005012915,0.013851346,0.0004339826],"about_ca_topic_score_codex":0.0000014755958,"about_ca_topic_score_gemma":0.0000040714954,"teacher_disagreement_score":0.1810205,"about_ca_system_score_codex":0.00016145036,"about_ca_system_score_gemma":0.000041804906,"threshold_uncertainty_score":0.7330833},"labels":[],"label_agreement":null},{"id":"W3129384632","doi":"10.1287/inte.2022.1132","title":"Optimization Helps Scheduling Nursing Staff at the Long-Term Care Homes of the City of Toronto","year":2022,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Scheduling (production processes); Status quo; Computer science; Absenteeism; Schedule; Long-term care; Operations management; Operations research; Nursing; Nurse scheduling problem; Business; Job shop scheduling; Medicine; Flow shop scheduling; Economics; Engineering; Management","score_opus":0.053871199729866065,"score_gpt":0.3475262837933807,"score_spread":0.2936550840635146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129384632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9432424,0.0014645103,0.039074782,0.0007212524,0.0010492966,0.00030365257,0.000063291314,0.00002169731,0.014059121],"genre_scores_gemma":[0.9974898,0.00007967621,0.0017530859,0.00009520938,0.00008377417,0.000003300844,0.0000059909503,0.000010562981,0.0004785766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99647975,0.00007697431,0.0009572504,0.00016645317,0.0020648413,0.00025472385],"domain_scores_gemma":[0.9971849,0.00044096168,0.0011930915,0.0006219572,0.0004700366,0.00008909954],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021329874,0.00014131816,0.00030756576,0.00013948226,0.0013013819,0.00015505953,0.0010618398,0.00005364784,0.0007062604],"category_scores_gemma":[0.0004984774,0.00007335527,0.00030127206,0.00075333554,0.00021736149,0.00017834084,0.00025514918,0.00044691426,0.000004946422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007923619,0.00007853279,0.005080554,0.0000052781525,0.000056267276,7.097838e-7,0.0023238068,0.9785236,0.00007993067,0.0021961546,0.00015256205,0.011423366],"study_design_scores_gemma":[0.008824462,0.0020298471,0.117467605,0.0010800678,0.0017928284,0.00063406373,0.25479302,0.5548309,0.02375463,0.024227586,0.008287432,0.002277572],"about_ca_topic_score_codex":0.00002305054,"about_ca_topic_score_gemma":0.000058020025,"teacher_disagreement_score":0.4236927,"about_ca_system_score_codex":0.0005341136,"about_ca_system_score_gemma":0.00028525243,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null},{"id":"W3152824712","doi":"10.1287/inte.2020.1070","title":"The Impact of Age Demographics on Interpreting and Applying Population-Wide Infection Fatality Rates for COVID-19","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Pandemic; Government (linguistics); Population; Workforce; Jurisdiction; Health care; Outbreak; Case fatality rate; Coronavirus disease 2019 (COVID-19); Demographics; Business; Public health; Geography; Demography; Political science; Economic growth; Medicine; Disease; Environmental health; Economics; Sociology; Nursing; Virology","score_opus":0.17293612935711253,"score_gpt":0.4690570621306652,"score_spread":0.29612093277355267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152824712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8945671,0.00009024285,0.102088556,0.001147894,0.00011931299,0.00072085176,0.00003323549,0.00006462446,0.0011682317],"genre_scores_gemma":[0.9964256,0.00042514905,0.0017890977,0.0012066418,0.000077731034,0.000030034884,0.000011877837,0.000015581032,0.000018244364],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99820834,0.00006709444,0.0009506682,0.00018314188,0.00028366898,0.00030711174],"domain_scores_gemma":[0.9862329,0.012325568,0.00084426906,0.00023800063,0.00017321514,0.00018604891],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0024997501,0.00022935418,0.00050678925,0.00013299329,0.0007358511,0.0001521258,0.00013495865,0.0001259155,0.000008441275],"category_scores_gemma":[0.021107528,0.00012790412,0.00035956406,0.00034874486,0.00013665717,0.00007976035,0.000105541374,0.00047388236,9.556741e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009250415,0.00027419874,0.6646909,0.0005985369,0.0014979583,0.000022040467,0.0008952028,0.03328879,0.00019363397,0.26890355,0.0034429876,0.02526711],"study_design_scores_gemma":[0.0013566269,0.0005009189,0.0971218,0.00019711496,0.0001973965,0.000035811754,0.0007378181,0.012169018,0.00020045774,0.8831177,0.0039527896,0.00041254645],"about_ca_topic_score_codex":0.000064131214,"about_ca_topic_score_gemma":0.00010899954,"teacher_disagreement_score":0.6142141,"about_ca_system_score_codex":0.00034041764,"about_ca_system_score_gemma":0.000105933,"threshold_uncertainty_score":0.9871381},"labels":[],"label_agreement":null},{"id":"W3158898498","doi":"10.1287/inte.2020.1055","title":"A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Supply chain; Computer science; Service level; Service management; Service (business); Stock (firearms); Risk analysis (engineering); Supply chain management; Operations management; Operations research; Reliability engineering; Business; Engineering; Marketing","score_opus":0.02284518305943014,"score_gpt":0.23545439099428783,"score_spread":0.21260920793485769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158898498","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64805675,0.0007603163,0.16551913,0.03497986,0.004445208,0.005779279,0.00017315615,0.0015468019,0.13873951],"genre_scores_gemma":[0.9932551,0.000028687049,0.00062873994,0.004503605,0.00092478323,0.000044083852,0.00011650726,0.00004374223,0.0004547719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997783,0.00000803717,0.0007533388,0.00028743903,0.00059523544,0.0005729265],"domain_scores_gemma":[0.9987187,0.000120088895,0.0005404651,0.00024505152,0.0003271949,0.00004849383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086408696,0.00030460773,0.00040547017,0.000732048,0.00042174515,0.0007242992,0.00038003316,0.000118724405,0.000085918495],"category_scores_gemma":[0.00013975179,0.00024026367,0.00018320516,0.0010847995,0.000026806672,0.00044760224,0.00012764645,0.0005726153,0.00009295291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005328731,0.00035946813,0.07355437,0.002019064,0.00024145703,0.00033688033,0.00033211647,0.82479864,0.00015219691,0.07954544,0.001859939,0.016267566],"study_design_scores_gemma":[0.0040502166,0.000051051727,0.0037539795,0.00048097176,0.0001394582,0.000020542622,0.007054566,0.8652311,0.0002566947,0.00068477204,0.11775713,0.00051952276],"about_ca_topic_score_codex":0.0001301186,"about_ca_topic_score_gemma":0.0006895226,"teacher_disagreement_score":0.34519833,"about_ca_system_score_codex":0.00019166958,"about_ca_system_score_gemma":0.00012362987,"threshold_uncertainty_score":0.97976685},"labels":[],"label_agreement":null},{"id":"W3162849139","doi":"10.1287/inte.2021.1080","title":"Theory-Driven Practical Approach to Integrate R&amp;D and Production Planning for Portfolio Management in Agribusiness","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Production (economics); Flexibility (engineering); Modern portfolio theory; Portfolio; Computer science; Function (biology); Operations research; Population; Agribusiness; Yield (engineering); Economics; Microeconomics; Mathematics; Agriculture; Geography; Statistics; Financial economics","score_opus":0.20873042411796147,"score_gpt":0.4650840388606759,"score_spread":0.25635361474271445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162849139","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056721725,0.000048406866,0.8808235,0.0008733376,0.00043430857,0.0006937617,0.0000050506474,0.000022917178,0.060377028],"genre_scores_gemma":[0.26678157,0.000066322194,0.7289169,0.0011075009,0.00023665198,0.00005975639,0.000011817709,0.000029048702,0.0027904406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971713,0.000094638344,0.0009283792,0.00045822718,0.0009866346,0.0003608598],"domain_scores_gemma":[0.99817586,0.0005172274,0.00036108197,0.00036519032,0.00034159442,0.00023905053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052879653,0.00021968214,0.00041096634,0.00068287045,0.000174056,0.0006981848,0.00029411385,0.00009452615,0.00003533115],"category_scores_gemma":[0.0018212433,0.00014922244,0.00009235606,0.0013480443,0.00006681858,0.00041013843,0.00016877083,0.000425862,0.00003637503],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030090471,0.0009974219,0.0009785332,0.00006656264,0.00033992823,0.00020519014,0.0032016793,0.16179234,0.0057823914,0.5119593,0.013892134,0.29777548],"study_design_scores_gemma":[0.0070391884,0.00087636965,0.013640241,0.0005267834,0.0003864904,0.0031177518,0.072800115,0.052104443,0.023180699,0.5596546,0.26411226,0.0025610467],"about_ca_topic_score_codex":5.3303376e-7,"about_ca_topic_score_gemma":9.1157483e-7,"teacher_disagreement_score":0.29521444,"about_ca_system_score_codex":0.00015970008,"about_ca_system_score_gemma":0.00010499316,"threshold_uncertainty_score":0.6732613},"labels":[],"label_agreement":null},{"id":"W3174371370","doi":"10.1287/inte.2021.1073","title":"Seasonal Inventory Management Model for Raw Materials in Steel Industry","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Raw material; Yard; Economic shortage; Business; Operations management; Environmental science; Supply chain; Operations research; Port (circuit theory); Inventory theory; Inventory control; Waste management; Engineering","score_opus":0.027895588298759915,"score_gpt":0.25822013543219885,"score_spread":0.23032454713343894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174371370","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8408613,0.00009058309,0.016704114,0.0031453709,0.0015738063,0.0015397726,0.000025076173,0.00015348551,0.13590649],"genre_scores_gemma":[0.9818395,0.00014479637,0.0008715288,0.011430793,0.0010330768,0.000059562055,0.00007578393,0.000044190983,0.0045007793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978262,0.0000046623895,0.0007136782,0.00028955628,0.00061151217,0.0005543802],"domain_scores_gemma":[0.9991004,0.000025668769,0.0003844587,0.00029199696,0.00014776262,0.00004972231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007679278,0.00028339858,0.00035589855,0.0005949012,0.00021988411,0.0007099793,0.0003996872,0.00019499526,0.00028913436],"category_scores_gemma":[0.000032555632,0.00023506534,0.00014512066,0.0005839175,0.00004735879,0.00062552805,0.00024216122,0.0004762814,0.00013005313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051615137,0.00061697717,0.0037347954,0.0007560034,0.00037646567,0.00039576684,0.0001640886,0.37226835,0.0004458597,0.5334334,0.055884324,0.03140782],"study_design_scores_gemma":[0.010044392,0.000057770325,0.009719956,0.00065143825,0.000550508,0.00003492373,0.006764917,0.67166406,0.0015006392,0.09731847,0.19986902,0.0018239248],"about_ca_topic_score_codex":0.000004517893,"about_ca_topic_score_gemma":0.000023712264,"teacher_disagreement_score":0.43611494,"about_ca_system_score_codex":0.00017053614,"about_ca_system_score_gemma":0.000080434766,"threshold_uncertainty_score":0.95856863},"labels":[],"label_agreement":null},{"id":"W3211496389","doi":"10.1287/inte.2021.1089","title":"Inventory Management Using a Weekly Review (<i>s</i>, <i>S</i>) Policy at the Bank of Canada","year":2021,"lang":"en","type":"review","venue":"INFORMS Journal on Applied Analytics","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Inventory management; Activity-based costing; Business; Unintended consequences; Actuarial science; Operations management; Finance; Economics; Accounting; Political science","score_opus":0.04584932154932919,"score_gpt":0.2831113914955137,"score_spread":0.23726206994618448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211496389","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000034642183,0.886242,0.00002469251,0.00064938754,0.0007524929,0.0011112132,0.000016778285,0.000024765206,0.11117524],"genre_scores_gemma":[0.000011154065,0.97605264,0.000042440188,0.016713694,0.0018235198,0.00004504126,0.000119875585,0.00010631382,0.005085324],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947141,0.000037221194,0.002293456,0.0004381107,0.0017068045,0.00081033906],"domain_scores_gemma":[0.9952953,0.00008085904,0.0031873288,0.001076947,0.00026575837,0.000093827875],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014977463,0.00086351496,0.002323097,0.00088285556,0.00055396784,0.00027374155,0.0013822635,0.00017428247,0.0008181091],"category_scores_gemma":[0.00009748497,0.00055150274,0.0010917467,0.0021992351,0.00013493067,0.00030206362,0.0011030961,0.00092093874,0.00010824074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000159222,0.00012770676,0.0000054352026,0.094543785,0.001983092,0.00025389763,0.000012667823,0.0005365436,1.5946426e-7,0.05238538,0.44614223,0.4039932],"study_design_scores_gemma":[0.0002966388,0.0000072908015,0.0000010112667,0.030141734,0.0026050035,0.00005396979,0.00012539283,0.00009043783,4.3445755e-7,0.00021921426,0.9659041,0.0005547306],"about_ca_topic_score_codex":0.004065172,"about_ca_topic_score_gemma":0.0040692086,"teacher_disagreement_score":0.5197619,"about_ca_system_score_codex":0.0018599314,"about_ca_system_score_gemma":0.0013084844,"threshold_uncertainty_score":0.99969363},"labels":[],"label_agreement":null},{"id":"W4211052544","doi":"10.1287/inte.1100.0522","title":"Contributors","year":2010,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Computer science","score_opus":0.006747082050140396,"score_gpt":0.2145491325432109,"score_spread":0.2078020504930705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4211052544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3361902,0.000062753024,0.329866,0.00047468022,0.0073761623,0.00030634555,0.000026228196,0.0012822847,0.32441533],"genre_scores_gemma":[0.9788625,0.00005528011,0.019908048,0.00038740144,0.0005470925,0.000002067415,0.00000699249,0.000028339588,0.0002022608],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999218,0.0000012184297,0.0002869777,0.000053577805,0.00022105395,0.00021919748],"domain_scores_gemma":[0.9995109,0.000034557375,0.000058462618,0.00013943262,0.000068666406,0.00018800388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020109775,0.00012520775,0.00014162713,0.00015716114,0.00009374001,0.00014748449,0.00015224959,0.00010918205,0.00020002111],"category_scores_gemma":[0.00003964119,0.000097797536,0.00006472139,0.00019070863,0.00002491448,0.000082904924,0.000008251657,0.0007990901,0.00025079557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001918368,0.000031076976,0.00019660198,0.000010178811,0.00012728457,0.000017201848,0.00015646836,0.93215007,0.000847513,0.0266146,0.002808417,0.037021402],"study_design_scores_gemma":[0.0017031542,0.000062695784,0.00040377307,0.000019031668,0.00004892376,0.000161693,0.00019729961,0.89815426,0.0068732826,0.0023378385,0.089473255,0.00056477427],"about_ca_topic_score_codex":1.7770022e-7,"about_ca_topic_score_gemma":9.415632e-7,"teacher_disagreement_score":0.6426723,"about_ca_system_score_codex":0.000033873916,"about_ca_system_score_gemma":0.000024941492,"threshold_uncertainty_score":0.39880678},"labels":[],"label_agreement":null},{"id":"W4213066012","doi":"10.1287/inte.1100.0553","title":"Contributors","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Computer science; Engineering","score_opus":0.13311805828135007,"score_gpt":0.34242367671483553,"score_spread":0.20930561843348547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213066012","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09273342,0.00007115349,0.06837631,0.001179576,0.00080919696,0.00018030177,0.00002026121,0.000079017525,0.8365508],"genre_scores_gemma":[0.9945184,0.00020975221,0.001342136,0.0012495094,0.00019688439,0.0000014635149,0.000001706376,0.000010863891,0.002469259],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961979,0.000030423294,0.0012858318,0.00022348815,0.0018577353,0.00040462593],"domain_scores_gemma":[0.9975036,0.0003611071,0.0007335827,0.0005630327,0.0004413912,0.00039730404],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003258963,0.00019919645,0.0004780735,0.00071521197,0.0003490049,0.00036832242,0.0010397683,0.0001244308,0.0021788196],"category_scores_gemma":[0.0007792591,0.00011467804,0.00040852872,0.0012163639,0.000114019094,0.00033367012,0.00007606931,0.0005366171,0.0032132268],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078330655,0.00035088026,0.014245048,0.000002907074,0.00063660083,0.0002137442,0.0030281548,0.0053554345,0.000077058794,0.33871195,0.038828067,0.5977669],"study_design_scores_gemma":[0.0017076539,0.00035374876,0.017250393,0.000016950571,0.00018810993,0.00013672141,0.0033660214,0.0054482734,0.001373473,0.6641202,0.30539843,0.00064001454],"about_ca_topic_score_codex":0.000005803106,"about_ca_topic_score_gemma":0.000008528873,"teacher_disagreement_score":0.901785,"about_ca_system_score_codex":0.00007827116,"about_ca_system_score_gemma":0.00010989061,"threshold_uncertainty_score":0.99873334},"labels":[],"label_agreement":null},{"id":"W4245764878","doi":"10.1287/inte.30.4.94.11649","title":"Book Reviews","year":2000,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Modeling, Simulation, and Optimization","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Commission; Space (punctuation); Library science; Ask price; Operations research; Data science; Political science; Engineering; Economics; Law","score_opus":0.046638770165563115,"score_gpt":0.3121370211433926,"score_spread":0.2654982509778295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245764878","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013468322,0.0020215483,0.3061726,0.0006768098,0.00036895287,0.0011627598,0.000008594718,0.00025577852,0.67586464],"genre_scores_gemma":[0.29564524,0.1159773,0.22328916,0.074688174,0.0070328214,0.000082731545,0.00016471988,0.00051415846,0.2826057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984006,0.0000132148025,0.0008552862,0.00011456076,0.00037310636,0.00024322911],"domain_scores_gemma":[0.99904823,0.00007606335,0.0003533735,0.00027753305,0.00008944944,0.0001553315],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058934896,0.0001884696,0.00032750552,0.00014287207,0.00021347181,0.00012850699,0.00018239983,0.00010590084,0.00482752],"category_scores_gemma":[0.00007014248,0.00013437419,0.00015258469,0.00019679064,0.000023624807,0.0002307638,0.000009405052,0.00033663205,0.0006030223],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021091543,0.00026281815,0.000052063337,0.00012495773,0.00013642639,0.0000118396465,0.0017141277,0.42513958,0.000011991349,0.09899281,0.32732433,0.14601815],"study_design_scores_gemma":[0.00071333017,0.000057428624,0.000009411009,0.0000784425,0.00007480719,0.000028009039,0.000028558241,0.10881025,0.00003809577,0.044794027,0.8451202,0.00024745672],"about_ca_topic_score_codex":4.5320635e-7,"about_ca_topic_score_gemma":9.5176233e-7,"teacher_disagreement_score":0.51779586,"about_ca_system_score_codex":0.000086065775,"about_ca_system_score_gemma":0.00004886237,"threshold_uncertainty_score":0.9960822},"labels":[],"label_agreement":null},{"id":"W4247553243","doi":"10.1287/inte.1110.0609","title":"Contributors","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Computer science","score_opus":0.0281790540709295,"score_gpt":0.24338214228324614,"score_spread":0.21520308821231665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247553243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03413164,0.00003414347,0.5653725,0.000027680866,0.00070305954,0.0001382815,0.0000060448774,0.00047279953,0.3991138],"genre_scores_gemma":[0.95518553,0.00008616591,0.04405455,0.0002970853,0.00017084712,0.0000023009047,0.0000025523739,0.00003918659,0.00016177772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990167,0.0000064832425,0.00040874633,0.000063686646,0.00022492523,0.00027944596],"domain_scores_gemma":[0.999448,0.00004118018,0.0000976102,0.00016452411,0.000069985785,0.00017871615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044099486,0.00014946042,0.00019052306,0.00019406682,0.00008581925,0.00006607749,0.00018966799,0.0000955131,0.00024844636],"category_scores_gemma":[0.000046713954,0.000121829544,0.00007623993,0.00025755545,0.000026132571,0.00011467699,0.000015303014,0.00043586193,0.0001859189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007732027,0.00006867868,0.0014287244,0.00003418427,0.000361335,0.000049828268,0.0013969503,0.8659715,0.00038929423,0.058200035,0.0041248933,0.06789727],"study_design_scores_gemma":[0.0045810402,0.00037688133,0.006503583,0.0001279067,0.00024318011,0.00038521586,0.0008869324,0.8470684,0.032662656,0.015892426,0.089378536,0.0018932145],"about_ca_topic_score_codex":4.1573688e-7,"about_ca_topic_score_gemma":2.5266903e-7,"teacher_disagreement_score":0.9210539,"about_ca_system_score_codex":0.00010402839,"about_ca_system_score_gemma":0.000023602306,"threshold_uncertainty_score":0.49680647},"labels":[],"label_agreement":null},{"id":"W4252228566","doi":"10.1287/inte.1110.0581","title":"Contributors","year":2011,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Business","score_opus":0.1919850467501503,"score_gpt":0.3883384410576747,"score_spread":0.19635339430752438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252228566","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059007175,0.0000138176965,0.22416864,0.0005201838,0.00027861694,0.0003194165,0.000017838573,0.00016126892,0.71551305],"genre_scores_gemma":[0.99372905,0.000016958305,0.0032473786,0.0013276793,0.00012407072,0.000006059312,0.0000017340836,0.000009384722,0.0015376854],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976736,0.0000111277695,0.00089601125,0.00016363064,0.001017567,0.00023807342],"domain_scores_gemma":[0.99815005,0.00027065462,0.0005023383,0.00046118678,0.00038340667,0.00023238633],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015857563,0.0001311759,0.00023164222,0.00039915694,0.00025398316,0.0002667014,0.00069805334,0.00009114058,0.0015561087],"category_scores_gemma":[0.00034926113,0.00008098644,0.00015875896,0.0007364049,0.00007032709,0.0002009624,0.00005381803,0.0003262318,0.0013236247],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009171303,0.00014051385,0.0031530422,8.984384e-7,0.0000463826,0.000010132737,0.0005335821,0.000885413,0.00008830355,0.81781596,0.026230132,0.15100391],"study_design_scores_gemma":[0.0006611028,0.00014832738,0.007924972,0.000008145595,0.000023932473,0.000043737942,0.00061853474,0.003383482,0.003591342,0.55497074,0.428341,0.0002846775],"about_ca_topic_score_codex":0.0000019607219,"about_ca_topic_score_gemma":9.626242e-7,"teacher_disagreement_score":0.9347219,"about_ca_system_score_codex":0.000058474237,"about_ca_system_score_gemma":0.000064387954,"threshold_uncertainty_score":0.99945396},"labels":[],"label_agreement":null},{"id":"W4256526873","doi":"10.1287/inte.31.3s.108.9685","title":"Implementing and Evaluating SilverScreener: A Marketing Management Support System for Movie Exhibitors","year":2001,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Movie theater; Attendance; Revenue; Path (computing); Path analysis (statistics); Computer science; Marketing; Advertising; Operations research; Business; Multimedia; Engineering; Economics; Art; Visual arts","score_opus":0.09065147209015902,"score_gpt":0.3933037154119106,"score_spread":0.3026522433217516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256526873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1890001,0.00004248667,0.4935339,0.0010434879,0.0005513352,0.0018341198,0.00003177525,0.00015627278,0.31380653],"genre_scores_gemma":[0.98690957,0.00004843987,0.009479712,0.00039062765,0.0003784137,0.000063163716,0.000006479502,0.000019192355,0.0027043812],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996454,0.00006984645,0.0014586288,0.00032277653,0.0011946894,0.00050004886],"domain_scores_gemma":[0.99675214,0.0012803173,0.0011085835,0.00034919838,0.00028547854,0.00022428828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019607345,0.00020422446,0.00039256466,0.000472722,0.0011327408,0.0007575897,0.0004313727,0.00006595641,0.00019971051],"category_scores_gemma":[0.00048577078,0.00014272296,0.00021425734,0.0006154975,0.00009889214,0.0002753528,0.00016912105,0.00022792871,0.00008225985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014519895,0.000039866714,0.0007542646,0.00004763413,0.00019075915,0.0000123935815,0.00028994613,0.005507017,0.00007832469,0.49809325,0.002392228,0.4911423],"study_design_scores_gemma":[0.02122956,0.00045224093,0.0013694329,0.00029898577,0.00071464945,0.00086748064,0.08792594,0.18117125,0.00061305356,0.15726551,0.5467725,0.0013193775],"about_ca_topic_score_codex":8.8608476e-7,"about_ca_topic_score_gemma":0.0000011360352,"teacher_disagreement_score":0.7979095,"about_ca_system_score_codex":0.0001206822,"about_ca_system_score_gemma":0.000044928933,"threshold_uncertainty_score":0.87122405},"labels":[],"label_agreement":null},{"id":"W4367676883","doi":"10.1287/inte.2023.1164","title":"Bombardier Aftermarket Demand Forecast with Machine Learning","year":2023,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Group for Research in Decision Analysis; HEC Montréal; Mila - Quebec Artificial Intelligence Institute; Bombardier (Canada)","funders":"","keywords":"Spare part; Demand forecasting; Computer science; Analytics; On demand; Process (computing); Economic shortage; Operations research; Data mining; Engineering; Operations management","score_opus":0.07595793931106345,"score_gpt":0.3423356359571896,"score_spread":0.2663776966461261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367676883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48240745,0.00005169157,0.15734275,0.003963057,0.0003207618,0.000909635,0.00007966144,0.0010611482,0.35386387],"genre_scores_gemma":[0.990665,0.000083531595,0.0036107316,0.00046255995,0.0001601943,0.000020054687,0.000013342555,0.000030107976,0.0049544615],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970991,0.000023816392,0.0007744792,0.00026831485,0.0013971931,0.00043708528],"domain_scores_gemma":[0.9980662,0.00045424444,0.0005376745,0.00043909502,0.00023773272,0.00026508205],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029597788,0.00022197055,0.00033971967,0.00067313464,0.00056151784,0.000646637,0.00067853683,0.000092813956,0.00025254808],"category_scores_gemma":[0.00031846276,0.00012655374,0.00015262005,0.0017099617,0.00011226648,0.00021844398,0.0001447228,0.0007327832,0.0007838725],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011218875,0.00017883333,0.040836606,0.000029989462,0.00038484056,0.00032637783,0.0012840462,0.13708551,0.00031625695,0.059421193,0.1879048,0.57110965],"study_design_scores_gemma":[0.001213261,0.00058185187,0.008751144,0.00008777774,0.00006685783,0.00043133122,0.00063741073,0.19870825,0.00049868226,0.071950294,0.71644396,0.00062918616],"about_ca_topic_score_codex":0.0000023362106,"about_ca_topic_score_gemma":0.000008895786,"teacher_disagreement_score":0.57048047,"about_ca_system_score_codex":0.00006108126,"about_ca_system_score_gemma":0.00007209552,"threshold_uncertainty_score":0.99999416},"labels":[],"label_agreement":null},{"id":"W4401952593","doi":"10.1287/inte.2023.0027","title":"Estimating Road Construction Costs with Explainable Machine Learning","year":2024,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministère des Transports; Polytechnique Montréal","funders":"","keywords":"Computer science; Transport engineering; Artificial intelligence; Machine learning; Engineering","score_opus":0.004908218599376482,"score_gpt":0.20715264379367995,"score_spread":0.20224442519430347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401952593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32121578,0.0005469901,0.48649743,0.00010020554,0.0045660916,0.00033878296,0.000009059944,0.001508968,0.18521668],"genre_scores_gemma":[0.9824277,0.000072492,0.016569352,0.000036776542,0.00069304736,0.000004725531,0.0000067590945,0.00004648099,0.00014262219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989615,0.0000031146599,0.0003118402,0.00010449052,0.00027466565,0.00034440347],"domain_scores_gemma":[0.9996477,0.000029606463,0.00006310069,0.00009640586,0.000051428462,0.000111796915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019690649,0.00020880395,0.00019076135,0.0002575128,0.00020244981,0.00037681177,0.0000994696,0.00007453658,0.00003752455],"category_scores_gemma":[0.000016300042,0.0001452808,0.000054863256,0.0002947841,0.000036030327,0.00027837767,0.000014901507,0.0010332768,0.000047233676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023118095,0.0000024333344,0.00020457791,0.00008230669,0.000118078286,0.00009582372,0.00023950373,0.81932914,0.0003023913,0.006011332,0.00017218429,0.17341909],"study_design_scores_gemma":[0.00044945927,0.00012957491,0.00010597009,0.00051210064,0.000062288585,0.001041953,0.0007052334,0.9756592,0.0023262121,0.0010782691,0.017562661,0.00036708024],"about_ca_topic_score_codex":0.0000033125234,"about_ca_topic_score_gemma":0.0000014501388,"teacher_disagreement_score":0.66121197,"about_ca_system_score_codex":0.0003344252,"about_ca_system_score_gemma":0.00004280887,"threshold_uncertainty_score":0.5924379},"labels":[],"label_agreement":null},{"id":"W4404536325","doi":"10.1287/inte.2023.0069","title":"Freight Gateway Consolidation for Purolator International Using Integer Programming","year":2024,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University; DuPont (Canada); Saint Mary's University","funders":"","keywords":"Consolidation (business); Integer programming; Gateway (web page); Computer science; Business; Operations research; Computer network; Engineering; World Wide Web; Finance; Algorithm","score_opus":0.028474612331637006,"score_gpt":0.300931678086638,"score_spread":0.272457065755001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404536325","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104366,0.000052689393,0.9823263,0.00010362536,0.0014319175,0.00022351573,0.000012980248,0.0003065037,0.005105859],"genre_scores_gemma":[0.77740484,0.000062244144,0.22127606,0.00015844672,0.00082557526,0.000015968853,0.000032886594,0.00008284328,0.0001411546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987722,0.0000066623834,0.0005386713,0.00012557623,0.00028985654,0.00026704048],"domain_scores_gemma":[0.9994108,0.00012829764,0.000093240196,0.00011368701,0.00013619335,0.00011774392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060444925,0.00018397954,0.00017899828,0.00042877463,0.000110264635,0.00063727907,0.00018607551,0.00011629084,0.00007887663],"category_scores_gemma":[0.00007967942,0.0001558867,0.0001234787,0.0003144426,0.000026414406,0.0003148315,0.000021015796,0.00043996307,0.00003119931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025518206,0.000014171359,0.00005471121,0.00007959618,0.00026537816,0.0000088038105,0.00024910623,0.91064274,0.001346511,0.016091345,0.0005561034,0.07066604],"study_design_scores_gemma":[0.00029932134,0.000025245487,0.0000082832275,0.0000974613,0.000051300027,0.0000715461,0.0000872753,0.9334801,0.0025473863,0.00095893635,0.062181443,0.00019172813],"about_ca_topic_score_codex":2.7896456e-7,"about_ca_topic_score_gemma":2.58403e-7,"teacher_disagreement_score":0.7669682,"about_ca_system_score_codex":0.00030794818,"about_ca_system_score_gemma":0.00007127048,"threshold_uncertainty_score":0.63568753},"labels":[],"label_agreement":null},{"id":"W4408906315","doi":"10.1287/inte.2023.0073","title":"OCP Optimizes Its Supply Chain for Africa","year":2025,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Supply chain; Business; Chain (unit); Marketing; Physics","score_opus":0.01710053685614881,"score_gpt":0.23301539891681167,"score_spread":0.21591486206066285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408906315","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02062769,0.0003041448,0.42423782,0.0006386466,0.0010668315,0.000739438,0.00007697019,0.0007448223,0.5515636],"genre_scores_gemma":[0.96305335,0.000727776,0.032625522,0.00035762886,0.0002484748,0.00004544591,0.000011099445,0.00004676344,0.0028839363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999097,0.0000012444945,0.0004295499,0.000085413936,0.00008081111,0.00030597456],"domain_scores_gemma":[0.99955195,0.00006841845,0.000076936536,0.00016420192,0.0000428183,0.00009569261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002799656,0.00016823954,0.00025142857,0.00027842293,0.000104353414,0.00013891373,0.00024597163,0.00011920748,0.000048605216],"category_scores_gemma":[0.000019562167,0.0001454384,0.00012504467,0.00014946739,0.000014697196,0.000088013185,0.000027229913,0.00029479925,0.000030066383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015504345,0.000046447854,0.00003565904,0.00017567823,0.00040577847,0.0000065440613,0.00039395428,0.5438176,0.00023067265,0.22858174,0.17998251,0.046168406],"study_design_scores_gemma":[0.0010569771,0.000111636466,0.000024048442,0.00010123337,0.00006715637,0.000019777784,0.0001959157,0.60633224,0.005524953,0.014843267,0.37133056,0.00039220497],"about_ca_topic_score_codex":1.8309927e-7,"about_ca_topic_score_gemma":6.4494446e-7,"teacher_disagreement_score":0.94242567,"about_ca_system_score_codex":0.00014934364,"about_ca_system_score_gemma":0.00003823325,"threshold_uncertainty_score":0.5930806},"labels":[],"label_agreement":null},{"id":"W4409800764","doi":"10.1287/inte.2024.0157","title":"Co-Creating an Analytical Mindset at a Financial Technology Platform","year":2025,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Mindset; Key (lock); Equity (law); Business; Venture capital; Finance; Process management; Knowledge management; Computer science; Political science; Computer security","score_opus":0.018469864682620414,"score_gpt":0.27203044353704703,"score_spread":0.2535605788544266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409800764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5363863,0.000018934224,0.0028237333,0.0006133443,0.00030676744,0.00024268813,0.00001063625,0.00021573056,0.45938188],"genre_scores_gemma":[0.9926962,0.000011276344,0.00040854255,0.0036392962,0.0006464126,0.000011516804,0.00004559786,0.000034651144,0.0025065015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99745864,0.0000015296326,0.0009271348,0.00035471335,0.00051573175,0.00074222486],"domain_scores_gemma":[0.9986254,0.000062157225,0.00063169503,0.00039781167,0.00022827568,0.000054644115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052038895,0.00040406774,0.0005368445,0.001902826,0.0006935682,0.0008832214,0.00069702737,0.00034461403,0.00016209247],"category_scores_gemma":[0.00046117484,0.00034170123,0.00018772538,0.0020004604,0.0002311068,0.0013231667,0.00023969433,0.00091244467,0.0006762577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041066724,0.00019890738,0.015248974,0.000107422464,0.00010298184,0.00011208149,0.000033294826,0.0006886283,0.0002282536,0.91640544,0.015453585,0.051009737],"study_design_scores_gemma":[0.0042771376,0.0002736199,0.011188943,0.0006358293,0.00035237413,0.00017542663,0.00083460484,0.049614668,0.0025787274,0.13615942,0.7922001,0.0017091439],"about_ca_topic_score_codex":0.000008943231,"about_ca_topic_score_gemma":0.00004277573,"teacher_disagreement_score":0.7802461,"about_ca_system_score_codex":0.00041708845,"about_ca_system_score_gemma":0.00015443885,"threshold_uncertainty_score":0.9999035},"labels":[],"label_agreement":null},{"id":"W4412573616","doi":"10.1287/inte.2024.0152","title":"Long-Term Open-Pit Mine Planning with Large Neighborhood Search","year":2025,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rio Tinto (Canada)","funders":"","keywords":"Term (time); Open-pit mining; Mining engineering; Computer science; Environmental science; Geology; Physics","score_opus":0.0202792902292711,"score_gpt":0.2782746126544821,"score_spread":0.257995322425211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412573616","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46127748,0.00010288078,0.19203761,0.00016425327,0.0002444162,0.00040181947,0.00001908288,0.00031457812,0.34543788],"genre_scores_gemma":[0.9957405,0.00013093755,0.0028849065,0.0004653158,0.00010204029,0.0000082437355,0.000014339955,0.000035183413,0.0006185877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887514,0.000002637314,0.00042156977,0.00012463586,0.00015099527,0.00042501878],"domain_scores_gemma":[0.99941915,0.00003652049,0.00007942403,0.00027968135,0.000042512504,0.00014271599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036123986,0.00021211858,0.0003087818,0.00030383226,0.00015118174,0.00044276682,0.0005508543,0.00011029884,0.000113229435],"category_scores_gemma":[0.000005920481,0.0001653827,0.00005552272,0.00026540548,0.000021528293,0.0002095303,0.00013166362,0.0006535416,0.00003922229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008310842,0.00033004323,0.08916429,0.00049695757,0.0018676243,0.00043436652,0.0014127773,0.62127995,0.00026427046,0.19490935,0.02849591,0.06051338],"study_design_scores_gemma":[0.0181732,0.0019682758,0.056708504,0.0036393672,0.0006194109,0.000826998,0.002299971,0.7845827,0.0121545885,0.011816344,0.10295572,0.004254968],"about_ca_topic_score_codex":0.0000010848187,"about_ca_topic_score_gemma":0.0000052867667,"teacher_disagreement_score":0.534463,"about_ca_system_score_codex":0.00017576742,"about_ca_system_score_gemma":0.000076384684,"threshold_uncertainty_score":0.6744111},"labels":[],"label_agreement":null},{"id":"W4414537747","doi":"10.1287/inte.2025.0247","title":"Redesigning Zoning Systems for Equitable and Efficient Last-Mile Delivery at Ninja Van","year":2025,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Software deployment; Zoning; Workload; Vehicle routing problem; Routing (electronic design automation); Key (lock); Voronoi diagram","score_opus":0.011573711498491434,"score_gpt":0.22742319398194966,"score_spread":0.21584948248345823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414537747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012925434,0.0003536074,0.9683882,0.000011018539,0.0003714484,0.00020077293,0.000006842738,0.00011787015,0.017624779],"genre_scores_gemma":[0.9888342,0.00033343848,0.009672101,0.000067769826,0.0000973826,0.000014233274,0.000011997147,0.000025818397,0.00094308075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989908,0.000003067599,0.00036991708,0.000112734946,0.00014663053,0.00037690013],"domain_scores_gemma":[0.9994611,0.00015396907,0.00010427202,0.00012016016,0.00006806744,0.00009243014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030867767,0.00016956154,0.00022304131,0.00023218185,0.00036878415,0.00016629227,0.000096323936,0.000088047855,0.0000043023624],"category_scores_gemma":[0.0000432954,0.00014700899,0.00004613422,0.00013146842,0.000025363328,0.00006468552,0.000051594805,0.00023038615,0.0000062842814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003638981,0.000007081788,0.000014539786,0.00010657064,0.000056185178,0.0000021005221,0.00007416927,0.98835474,0.00024563045,0.008367394,0.0006780912,0.0020571298],"study_design_scores_gemma":[0.0006898752,0.00005190197,0.000018074195,0.00014887711,0.000059906728,0.000013368274,0.0003213867,0.98361653,0.0017710074,0.0011906588,0.011915063,0.000203363],"about_ca_topic_score_codex":7.23144e-7,"about_ca_topic_score_gemma":6.125898e-7,"teacher_disagreement_score":0.97590876,"about_ca_system_score_codex":0.0002761857,"about_ca_system_score_gemma":0.000024548446,"threshold_uncertainty_score":0.5994853},"labels":[],"label_agreement":null}]}